Jobs in Ramat Gan

Description Alice's RL Environments team builds adversarial environments that the world's leading AI labs use to test and train their most capable agents. Each environment is a working replica of a real system with an attack planted inside it. A verifier decides whether the agent held up. All of it runs on AgenticVerse, our platform for building, running, and reviewing RL environments. AgenticVerse is a full-fledged, production-grade SaaS application. A large and fast-growing community of researchers works in it every day. We're looking for a Senior Full-Stack Engineer to own AgenticVerse as a product, and to build the digital twins our environments are made of. What you'll do AgenticVerse • Own the AgenticVerse application end to end: frontend, backend, data model, and APIs • Build the workflows researchers live in: authoring tasks, launching runs, managing gyms, and reviewing results • Make a run readable at a glance: traces, verifier results, and where the agent went wrong • Hold a production bar: performance, reliability, access control, and observability as the user base grows • Take features from design to production, and talk to the researchers who use them Digital twins • Build digital twins of real products: web apps an agent can't tell apart from the real thing • Build the full stack behind each one: the UI, a real API backend, and the state that persists across a run • Expose every action the agent takes as an event the verifier can grade Requirements What we're looking for • 6+ years as a full-stack engineer, building production SaaS • BSc in Computer Science or a related field, preferably from a leading university • Strong TypeScript and React, and a backend language (Python preferred) • Experience designing APIs and data models for multi-user applications • Production experience with Docker and cloud infrastructure (AWS preferred) • High autonomy in an undefined space. You take a loose problem and come back with working code • Fluent written and spoken English Advantage • Hands-on AI engineering: building with LLMs, agents, or evaluations • Experience building developer tools or internal platforms • Experience with Kubernetes • Background in security, red teaming, or trust and safety About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description Alice secures AI apps, agents and models from build to production. Enterprises are shipping AI faster than they can secure it, attack techniques evolve constantly, and every company draws the line on acceptable behavior in a different place. No enterprise can keep up with that on its own, and Alice is how they do it. At the heart of the platform sit two engines: the attack engine finds the failures before attackers do, and the defense engine blocks them in production without breaking legitimate traffic. We are hiring a Senior PM who will lead the strategy, vision, and execution of our core engines. This PM sits inside the DS workflow — and can read the notebooks, argue about the eval set, query the data themselves — and translate between model reality and customer reality in both directions. This is not a PM role that writes a PRD and waits. It is a PM role where “I pulled the data, here’s what moved the F1, and here’s what it means for customers” is the expected level of answer. What you'll own Attack engine roadmap. Coverage and depth of adversarial techniques, novel-attack generation quality, agentic/multi-turn testing, zero-day and model-risk research productization. You decide which harm categories, modalities, and attack families we invest in next, and you defend that call with evidence. Defense engine roadmap. Classifier quality across policies and languages, the precision/recall/latency triangle, customer-specific tuning, and the path from "a red team finding" to "a guardrail that blocks it." Closing that attack→defense loop is the single highest-leverage thing this role can do. Evaluation as a product surface. Own how we define, measure, and publish model quality — benchmark datasets, eval harnesses, golden sets, regression gates, and the metrics customers actually see. When a customer asks "how accurate is this," the answer and the evidence behind it are yours. Data strategy for the engines. What data we collect, label, retain, and feed back into training; how customer feedback (overrides, false-positive reports, session feedback) becomes a training signal; and how all of it stays within our customer data commitments The DS↔Engineering seam. Model artifacts, schemas, and pipelines. You keep that contract explicit and get research into production. Day to day • Run discovery with AI Product Owners, security engineers, and CISOs on what attacks they fear and what false positives they can't tolerate • Turn fuzzy customer harm into a testable engine requirement with a measurable bar • Prioritize the DS backlog with the DS lead; make the hard calls on coverage vs. depth vs. latency • Own the quality narrative for sales, solution engineering, and security reviews — backed by numbers you can defend • Partner with design and platform PMs so engine capability becomes a usable product, not a config flag • Track competitive and academic movement in adversarial ML and guardrails, and translate it into roadmap pressure Requirements Required • 5+ years in product management, with at least 2 shipping ML/data-science-backed products • LLM-era experience: evals, prompt engineering, RAG, agentic systems, LLM-as-judge and its failure modes • You can reason about model evaluation properly: precision/recall tradeoffs, class imbalance, train/test leakage, annotation quality, why a benchmark number is or isn't trustworthy • Experience defining and defending model quality metrics to customers or regulators • A track record of working with data scientists and research engineers Strongly preferred • Security or adversarial ML background • Familiarity with data pipeline tooling/feature and labeling infrastructure • Experience with enterprise buyers in regulated industries About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description We are seeking a highly technical, hands-on Principal AI Research Cybersecurity Scientist to drive fast-paced research in frontier AI models, benchmarking, and evaluation. The ideal candidate combines strong research credentials with exceptional engineering capabilities and a proven ability to execute rapid research cycles, moving quickly from identifying research questions to building prototypes, running experiments, and delivering actionable results. The role requires deep expertise in LLMs, foundation models, reasoning, agents, and model evaluation, alongside the ability to translate complex customer challenges into measurable research outcomes. Key Responsibilities • Conduct hands-on research in frontier AI models, reasoning, agents, and multimodal systems. • Design, implement, and iterate on AI benchmarks, evaluation frameworks, and experimental pipelines. • Execute short, high-impact research cycles, rapidly moving from hypotheses to experiments, prototypes, and validated findings. • Identify model limitations, evaluation gaps, and emerging research opportunities. • Work directly with customers and technical stakeholders to translate complex challenges into actionable research. • Collaborate with engineering and product teams to turn research findings into practical solutions. Requirements Qualifications • PhD or MSc in Computer Science, AI, Machine Learning, or a related field, with strong relevant research experience. • Strong publication record at leading AI/ML conferences such as NeurIPS, ICML, ICLR, ACL, or equivalent venues. • Exceptional hands-on technical capabilities, including advanced Python, PyTorch, and experience building and evaluating complex AI systems. • Proven ability to independently design, implement, and execute research experiments within short development cycles. • Deep understanding of LLMs, foundation models, benchmarking, and evaluation methodologies. • Experience with frontier AI labs such as OpenAI, Anthropic, or Google DeepMind is a strong advantage. • Strong analytical and communication skills, with the ability to operate effectively in a fast-paced, customer-driven research environment. Preferred Qualifications • Experience with AI for Cybersecurity, including autonomous security agents, vulnerability research, penetration-testing automation, or security operations. • Experience with Security of AI, including LLM red teaming, adversarial machine learning, prompt injection, and agentic AI security. • Familiarity with cybersecurity tools and environments such as CTFs, sandboxed exploitation environments, static/dynamic code analysis, and security testing frameworks. • Experience developing cybersecurity benchmarks or evaluating frontier models on complex security-related tasks. About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description Alice's RL Environments team builds adversarial environments that the world's leading AI labs use to test and train their most capable agents. Each environment is a working replica of a real system with an attack planted inside it. A verifier decides whether the agent held up. The environments run on AgenticVerse, our platform for building and running RL environments at scale. A large and fast-growing community of researchers works on it every day. We're looking for a Senior AI Engineer to own those environments and the evaluation that decides which tasks are good enough to train a frontier model. What you'll do RL environments • Build RL environments end to end: the replica systems, the planted attack, and the verifier that grades the run • Own environment and verifier design for tool-use, web, and computer-use agents • Make environments airtight: sandboxed, free of eval-awareness cues, and closed to reward hacking • Read rollout traces, find where the agent took a shortcut, and close it Task evaluation • Own Alice's proprietary task evaluator, the engine that decides whether a task is ready for a frontier lab • Make it the industry's sharpest quality bar for RL tasks: realism, threat-model accuracy, and training value • Keep it learning: every customer review and every post-training run makes it stricter Run infrastructure • Scale the run pipeline: queue-driven runners on Kubernetes, per-run sandboxes, and a grader the agent can't reach • Package environments so they drop into customer training and evaluation pipelines Working with Research • Work with researchers as one team, from a task idea to an environment that's ready to train on • Turn what post-training runs reveal into fixes in the environments • Review designs and code, and raise the engineering bar for the people around you Requirements What we're looking for • 6+ years as a backend engineer, building production systems • BSc in Computer Science or a related field, preferably from a leading university • Strong Python, and production experience with Docker, Kubernetes, and cloud infrastructure (AWS preferred) • Experience designing distributed systems and message queues • A track record of owning systems from design to production • High autonomy in an undefined space. You take a loose problem and come back with working code • Fluent written and spoken English Advantage • Academic background in AI or machine learning: a degree, thesis, or research • Hands-on AI engineering: building with LLMs, agents, or evaluations • Exposure to post-training: RL fine-tuning, reward design, or rollout analysis • Background in security, red teaming, or trust and safety About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description Alice is at the forefront of a shift in online safety. As Big Tech moves beyond reactive moderation, Alice is building the agentic future of Trust & Safety. We are hiring a Director of Product to close the gap between our Trust & Safety expertise and the automated, repeatable products the world's largest platforms now need. This is a high-impact leadership role focused on incubating new ideas, pushing from 0 to 1, and productizing what works. You will turn bespoke moderation workflows and investigation methodologies into a portfolio of repeatable, data-driven offerings that let global platforms run agentic safety systems at volume. This role is not red teaming and it is not research. It is execution and scale. You will work closely with the pod leadership to capture domain expertise and embed it into the Alice product suite. Core responsibilities • Productization strategy. Identify high-value Trust & Safety & Security workflows inside Big Tech environments and architect their transformation into standardized product offerings that hold up at platform volume. • Maintain the data moat. Define Alice's data assets as the durable differentiation across all of the company's offerings, services and product lines. • Agentic moderation. Lead the product roadmap for the transition from manual and basic-automated moderation to agentic systems capable of autonomous investigation and enforcement. • Scaling for Big Tech. Design offerings for the infrastructure and policy requirements of big tech platforms like -high throughput, low friction to adopt. • Deliver bespoke projects. Secondary, but real: lead demand and learn from existing and incoming projects, covering the full cycle of requirement collection, development and delivery. Requirements • Proven track record in product management or leadership within the Trust & Safety or Security domains, with a demonstrated ability to lead cross-functional teams • Strategic product leadership: experience leading cross-functional teams to deliver 1-to-N product growth, with a focus on repeatability, efficiency and market fit • T&S scaling: a proven track record of taking complex service-based operations or internal tools and scaling them into revenue-generating, production-ready products • Big Tech literacy: deep understanding of Trust & Safety ecosystems, policy constraints and operational friction points within major platforms • Agentic systems: strong conceptual and practical understanding of how LLM-driven agents can be deployed for content moderation and deep-dive investigations • Entrepreneurial execution: able to operate at a high strategic level while holding the technical depth to judge product feasibility • Communication: mastery in distilling complex technical capabilities into clear, value-driven narratives for senior stakeholders at Fortune 500 companies About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description As an Adversarial AI Evaluation Scientist, you will review and validate adversarial agent tasks: indirect prompt-injection (IPI), data-exfiltration, tool-misuse, and related attack scenarios to ensure each one is realistic, fair, deterministic, reproducible, and correctly graded. You will decide whether a scenario genuinely demonstrates a model vulnerability or merely an artifact of how the task was built, and you will combine adversarial-security judgment with statistical analysis and LLM-assisted tooling to find quality issues, analyze attack-success patterns, and recommend or automate improvements. Responsibilities • Review adversarial tasks for realism and threat-model soundness: is the attacker’s capability plausible, can the attacker actually reach the poisoned content, and is the target system a realistic one? • Assess attack fairness and attribution: confirm the harm is driven by the adversary (not pre-baked into the system prompt, vouched for by a trusted layer, or otherwise rigged), so a careful agent could plausibly pass. • Validate grading correctness: verify that success/failure criteria, reward logic, and deterministic checks detect the intended harm, with no reward hacks, token-collapse, or evadable gates. • Measure and interpret attack success rate (ASR@K) and its variance; verify deterministic, reproducible behavior across repeated executions. • Judge attack quality - novelty vs. known patterns, sophistication, stealth, and coverage across harm types and target surfaces. • Identify anomalies and systemic issues across large sets of tasks (e.g. scenarios that succeed ~100% of the time against every model - a red flag for an unfair task, not a win). • Define validation guidelines, acceptance criteria, quality thresholds, and release gates for adversarial tasks. • Use LLMs and AI models to analyze attack failures, identify root causes, and assist with task or grader fixes. • Build automated validation, regression, and quality-monitoring pipelines for adversarial task sets. • Work closely with red-teamers, researchers, and task authors to improve attack design, fairness, and reliability. Requirements Must Have • 4+ years in an analytical, ML, security-research, or applied-research role, including hands-on work with adversarial ML, LLM security, red-teaming, or AI safety evaluation. • Bachelor’s or Master’s in Computer Science, Machine Learning, Statistics, a related quantitative field, or equivalent practical experience. • Working knowledge of prompt injection / indirect prompt injection, jailbreaks, data exfiltration, or tool-misuse attacks against LLM-based systems or agents. • Strong Python and data-analysis skills. • Solid grounding in statistics and experiment design: distributions, variance analysis, hypothesis testing, and anomaly detection: applied to evaluation and attack-success measurement. • Experience building or auditing data-driven validation, evaluation, or quality frameworks. • Experience using LLMs for analysis, evaluation, code generation, or automated remediation. • Strong understanding of reproducibility, deterministic systems, and automated testing. • Sharp adversarial thinking and the ability to spot subtle ways a test can be unfair, gameable, or mis-graded. • Excellent analytical, problem-solving, and communication skills. Preferred Skills That Set You Apart • Certifications in offensive cybersecurity (e.g., OSWA, OSWE, OSCE3, SEC542, SEC522) • Experience designing or evaluating AI agents, LLM-powered applications, or agentic workflows (e.g. email/doc assistants, tool-using agents). • Familiarity with RL Gym-style task formats, Gymnasium/OpenAI Gym, or agent-evaluation harnesses. • Experience red-teaming production AI systems or building adversarial benchmark datasets. • Experience with automated evaluation pipelines, regression testing, and quality gates. • Familiarity with safety/security taxonomies (harm types, attack patterns) and threat modeling. About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description Alice is a leading platform for Trust & Safety teams worldwide, leveraging cutting‑edge AI and world‑class expertise to protect users from the widest spectrum of online harms. By continuously collecting and analyzing data, our solutions ensure customers stay two steps ahead of bad actors in an ever‑changing threat landscape. We’re seeking an outstanding Backend Engineer with a Generative AI orientation to join our research team. We are building a new AI Platform from scratch to standardize AI development methods, ensure excellence and efficiency, and deliver robust, scalable, and compliant AI capabilities. This platform will handle GenAI models deployment, prompt management, data filtering, logging, and monitoring, centrally, so individuals can focus on their own business-specific logic. Key Responsibilities • Build and maintain CI/CD pipelines that support fast, reliable integration and deployment of GenAI systems across complex environments, including orchestrating multiple models/agents and MCP clients/servers. • Develop, deploy and integrate AI solutions and agentic frameworks, creating both command‑line tools and interactive notebooks that support our team’s research and workflow automation needs. • Build and manage data pipelines and databases (SQL) for large‑scale data handling, automating data collection and curation (e.g., adversarial prompts for model red-teaming) to support AI training and evaluation. • Integrate research into production by implementing the latest generative AI advancements (including white‑box LLM development), integrating different models and agents, and developing evaluation frameworks (e.g., agentic reasoning tests) as part of our automated workflows. • Integration & Orchestration: Integrate dockerized environments (e.g., Harbor format) into internal training frameworks, optimizing reset/statefulness semantics, concurrency, and throughput ceilings • Deploy GenAI models from repositories such as HuggingFace onto our AWS platform, connect them to data pipelines, and automate end‑to‑end processes for prompt generation, labeling, and response evaluation. Requirements Must-to-have • B.Sc. in Computer Science, Computer Engineering, or a related field (or equivalent hands‑on experience). • 5+ years of experience delivering large‑scale, high‑performance systems on AWS, with an emphasis on orchestrating data pipelines and AI workflows in production environments. • Expertise in scripting and automation using Python and shell; proven ability to write clean IaC and CI/CD pipelines. • Strong understanding of Linux systems, networking, and distributed system design. • Ability to break down monolithic systems into scalable, loosely coupled services and microservices. • Strong cross‑functional communication and collaboration skills, with a DevOps mindset to drive best practices across teams. • Hands‑on experience deploying and integrating generative AI models (e.g., large language models or other AI/ML models) in production. • Hands‑on experience deploying MCP clients/servers and integrating them into automated AI workflows. Nice-to-Have • M.Sc. in Computer Science, Computer Engineering, or a related field. • Experience with large‑scale cluster management or HPC scheduling tools (e.g., Slurm). • Knowledge of advanced observability and monitoring tools such as Prometheus and Grafana. • Experience with cloud cost optimization (FinOps concepts) and exposure to infrastructure security tools or configuration management for compliance. • Hands‑on experience working with A2A protocol. Alice is the leading provider of security and safety solutions for online experiences, safeguarding more than 3 billion users, top foundation models, and the world’s largest enterprises and tech platforms every day. As a trusted ally to major technology firms and Fortune 500 brands that build user-generated and GenAI products, Alice empowers security, AI, and policy teams with low-latency Real-Time Guardrails and a continuous Red Teaming program that pressure-tests systems with adversarial prompts and emerging threat techniques. Powered by deep threat intelligence, unmatched harmful-content detection, and coverage of 117+ languages, Alice enables organizations to deliver engaging and trustworthy experiences at global scale while operating safely and responsibly across all threat landscapes. About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description You will define Alice's research agenda in AI safety, security, and model evaluation - and drive the science that delivers it. The benchmarks you design will become the new world-standards. The methods you develop will run inside the labs defining what AI becomes. Specifically, the org you will be leading technically, is Alice Labs, that includes AI researchers from across Alice departments and directly within the Alice Labs department. This is a role for someone whose strength is in scientific pragmatism. Pushing the envelope on how new frontier research should apply to AI safety and security. You will ship three new benchmarks/evals every month, one breakthrough benchmark each quarter, and at least one paper per quarter at top-tier conferences (eg. NeurIPS, ICML, ICLR, ACL, or IEEE S&P etc) You will also represent Alice externally - at conferences and events, with research partners, and on visits to frontier labs. Travel is an important part of this role: it is how science stays sharp and how Alice stays at the frontier. What you will own • The research roadmap - safety, security, red teaming, adversarial robustness, LLMs, agents, and multimodal systems and more. • Benchmark and evaluation design - frameworks and methods that surface real vulnerabilities in frontier models • Research relationships with leading frontier labs and academic institutions • Collaborative engagements with technical teams at partner organizations • Alice's published research output and scientific reputation • The pipeline from research to product - working with engineering and product to ship findings at scale Requirements • A research leader whose primary strength is scientific, yet pragmatic and grounded in the real needs of the future - someone who has worked at the frontier and wants to push it further • Deep expertise in AI safety, security, red teaming, or adversarial robustness • A strong publication record at leading AI or security venues • Experience mentoring or leading senior researchers • Comfort engaging with technical partners and collaborators • Willingness to travel, including internationally • PhD in AI, ML, CS, or security is a strong advantage - a compelling research record without one is equally welcome About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description Alice is seeking to hire a Technical Fraud Threat Analyst to join the Fraud team. In this role, you will research, evaluate, and analyze fraud detection mechanisms, abuse vectors, and platform vulnerabilities across a wide range of online services. You will generate strategic insights from diverse intelligence sources, conduct OSINT investigations, and identify emerging fraud tactics and behavioral vulnerabilities. In addition to research and analysis, you will leverage technical tools and infrastructure to support investigations, simulate real-world abuse scenarios, and enable scalable fraud research. Your work will culminate in actionable intelligence and mitigation recommendations for clients. Key Responsibilities: • Conduct investigations and identify fraud methods across web and mobile platforms (social media, forums, apps, and the dark web). • Design and execute fraud research methodologies, including technical validation of abuse scenarios. • Build, maintain, and operate research environments and technical tooling that support fraud investigations and red team activities. • Analyze large datasets, telemetry, and technical artifacts to uncover patterns and deliver actionable insights. • Develop or leverage automation to improve research workflows and increase operational efficiency. • Monitor third-party websites and channels for malicious activities and fraud risks. • Prepare detailed reports with findings, insights, and actionable recommendations. Requirements Required Skills & Experience: • 3+ years of experience in intelligence analysis, CTI, fraud detection, security research, or a similar technical investigative role. • Strong understanding of OPSEC principles. • Strong knowledge of WEBINT and OSINT, including social media, apps, and the dark web. • Comfortable working with technical environments, logs, APIs, automation, and investigative tooling. • Ability to troubleshoot technical issues and rapidly learn new technologies, tools, and research methodologies. • Strong analytical, communication, and reporting skills. • Proactive, creative, and quick to learn new tools and practices. • Fluent in English (additional languages are a plus). • Comfortable with sensitive content. Advantage: • Experience conducting red team exercises on online platforms to identify abuse methods and bypass mechanisms. • Experience with scripting or programming (e.g., Python, JavaScript, or similar) to support research and automation. • Experience developing or maintaining research infrastructure, automation pipelines, or investigative tooling. • Familiarity with cloud environments, virtualization, or containerized environments. • Platform familiarity: Hands-on experience with social media, apps, and forums. • Experience working with APIs, web technologies, or automation frameworks. • Experience with multiple languages. About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description We are seeking a Senior AI Researcher to lead post-training evaluation, red-teaming, and reinforcement learning (RL) gym audits on open-weight models. The ideal candidate will establish rigorous benchmarking methodologies, evaluate large language models (LLMs) against complex threats like Indirect Prompt Injections (IPI), and construct post-training evaluation pipelines that accurately measure realistic frontier-level security capabilities. Key Responsibilities RL Post-Training & Benchmarking • Execute post-training runs (e.g. GRPO) using mainstream open-weight generalist models against security-focused RL environments, targeting threat vectors like Indirect Prompt Injection (IPI). • Reward Diagnostics & Trace Analysis - Analyze live loss curves and rollout traces to identify reward hacking, lazy policy convergence, and flawed or over/under-specified verifiers. Task & Environment Auditing • Review tasks and multi-turn environments (including tool use, web navigation, and computer use) for realism, threat model accuracy, data distribution, and dataset balance. Performance Reporting (Gym Cards) • Generate comprehensive evaluation cards detailing hill-climbing performance uplift across checkpoints, failure modes, tokens/turns per rollout, and task-level success rates. Integration & Orchestration • Integrate dockerized environments (e.g., Harbor format) into internal training frameworks, optimizing reset/statefulness semantics, concurrency, and throughput ceilings. Requirements Required Qualifications • Technical Background: M.S. or Ph.D. in Data Science, Machine Learning, Computer Science, or equivalent practical experience in deep learning. • RL & Post-Training Expertise: Strong hands-on experience training large-scale models using RL algorithms (e.g. GRPO, PPO) on open-weight architectures. • AI Security Expertise: Solid understanding of LLM vulnerabilities, red-teaming methodologies, and defensive alignment against IPI attacks. • Infrastructure Skills: Proficiency in PyTorch, Docker containerization, and distributed training architectures. • Diagnostic Skills: Ability to analyze agent rollout traces, craft deterministic rubrics/verifiers, and debug complex reward shaping flaws. Preferred Qualifications • Prior experience working with standard RL gym formats, such as Harbor. • Experience evaluating complex agentic workflows in tool-use or web-browser environments. • Familiarity with evaluating open-weight models similar to Llama or Mistral against adversarial workloads. About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description Alice is seeking to hire a Fraud Threat Analyst to join the Fraud team. In this role, you will be responsible for researching, evaluating, and analyzing fraud detection mechanisms and vulnerabilities across various platforms and solutions. You will generate strategic insights from diverse intelligence sources, conduct OSINT investigations to identify fraud risks, and monitor online platforms to detect malicious activities. You will also focus on uncovering fraud tactics, behavioral vulnerabilities, and process bypasses while delivering actionable mitigation recommendations to clients. Key Responsibilities: • Conduct investigations and identify fraud methods across web and mobile platforms (social media, forums, apps, and the darknet). • Analyze large datasets to uncover patterns and deliver actionable insights. • Monitor third-party websites and channels for malicious activities and fraud risks. • Prepare detailed reports with findings, insights, and actionable recommendations. Requirements Required Skills & Experience: • 3+ years of experience in intelligence analysis, CTI, or fraud detection. • Strong understanding of OPSEC principles. • Strong knowledge of web intelligence (WEBINT) and OSINT, including social media, apps, and the dark web. • Strong analytical, communication, and reporting skills. • Proactive, creative, and quick to learn new tools and practices. • Fluent in English (additional languages are a plus). • Comfortable with sensitive content. Advantage: • Experience in conducting red team exercises on online platforms to uncover potential abuse methods and bypass mechanisms • Experience with multiple languages. • Platform familiarity: Hands-on experience with social media, apps, and forums. • Experience with automation tools or scripting. About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description At Alice, our Cyber Threat Intelligence group thrives on curiosity and deep technical dive capabilities. We are looking for a Senior Threat Intelligence Researcher with a passion for CTI and a background in proxy research/ anti-bot defense/ web-scraping /threat hunting, with a proven ability to take initiative and conduct independent research with a track record of high-level results. In other words, not just an individual contributor, but a true talent that is seeking to make a mark in the industry. You will lead technical threat research, author analytical reports, and proactively monitor ongoing and emerging threats to keep us steps ahead of adversaries. Responsibilities • Hunting, detecting, monitoring, and researching threats against Alice clients. • Analysis of threat groups and TTPs across different fraudulent ecosystems. • Researching and monitoring multiple sources through the clear, deep, and the dark web. • Produce technical reports for clients describing threat actor TTPs, analysis and exploits relevant for Alice’s clients. Work closely with client account leads and stakeholders, as well as collaborating with fellow analysts to produce intelligence reports. • Review, enrich and provide analyst tradecraft for other team members as part of the delivery process. Prepare assessments of current threats and trends based on collection, research and analysis of classified intelligence collected by the teams. Requirements Must Have • Experience in one of the following fields: • - Anti-bot detection/research • - Proxy detection/research • - Web scraping development/detection/prevention • Experience in technical CTI threat hunting - databases, IPv4/IPv6 intelligence, • Experience with threat hunting platforms: VirusTotal, UrlScan, Greyhat Warfare, Shodan • Experience in analyzing threat groups and actors using OSINT, WEBINT methodologies • Darkweb and Cybercrime intelligence experience • Strong English communication skills: ability to clearly communicate intelligence by writing reports, or briefing clients and other stakeholders • Must have demonstrated experience in gathering and evaluating internet information from social media, chats and darknet forums • Great verbal and written communication skills Nice to Have • Scripting (Python, BASH, and/or equivalent) • Experience in operating threat intelligence collection platforms: Intel471, Recorded Future, CyberSixgill, Flashpoint and/or equivalent • Foreign language proficiency About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description We are hiring a Principal Data Scientist for the Data Science group, reporting directly to the VP Engineering. This is a senior individual-contributor leadership role: you own the technical direction and architecture of what the group ships - real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath - and you lead the hardest projects end to end, hands on in the code. What you'll own A large Python, Rust, and PySpark monorepo, dozens of production services and shared libraries, across three connected domains: Content-moderation inference at scale Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through our in-house ActiveServe framework over NVIDIA Triton and a Rust detection service, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models. GenAI safety and red-teaming Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives, and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok). Data platform and MLOps An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection. What you'll do • Own the GenAI safety roadmap - decide what we build with LLMs and what we build around them: guardrails and LLM-as-judge adjudication, prompt-injection and jailbreak detection, policy authoring, and the automated red-teaming that measures attack-success rate against customers' applications. Choose which model families and providers we use, fine-tune, or retire, and what each has to prove before it reaches customer traffic. • Own how we evaluate GenAI systems - benchmark and dataset design over real production traffic, LLM-jury labeling protocols, multilingual, multi-turn, and multimodal coverage, calibration and threshold selection, and the error analysis that turns a metric into a fix. Run the evaluations that separate a real shift in what these models can do from a launch post, and keep quality honest in production as traffic, prompts, and attacks drift. • Lead the hardest GenAI work end to end, and grow the craft - take the ambiguous, high-stakes projects from framing through prototype, offline evaluation, shadow traffic, and rollout; mentor data scientists and ML engineers through design, experiment, and model review; and translate model behavior into what Product and customers care about: accuracy, latency, cost, and enterprise readiness. Requirements What we're looking for • ML technical leadership - a proven tech lead, senior data scientist, or ML engineer who has taken multi-person, multi-quarter ML initiatives from framing to production without formal authority, with strong Python and the written communication to make the case in a design or an evaluation. • Deep understanding of LLM technology - how these models are trained, adapted, and served, what prompting, fine-tuning, RAG, and agentic patterns each buy you, where they fail, and how to measure quality, latency, and cost. • AI-augmented engineering - deep, daily fluency with an AI coding assistant (Claude Code, Cursor, or Codex), with the judgment to raise the whole team's leverage and set how these tools are used well. About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description About the Role We are looking for a Head of RL Gym to own the end-to-end delivery, quality, and scaling of RL Gym programs for our strategic AI partners. You will work closely with frontier-model teams, Delivery, Product, Sales, Research, and Operations to turn complex customer and model-development needs into high-quality, scalable programs. What You’ll Own • Own partner delivery from scoping through launch, execution, scaling, and iteration. • Translate research and model needs into clear program requirements, workflows, and success metrics. • Work closely with frontier-model teams to identify model gaps and design RL Gym programs that address them. • Own quality standards and quality assurance, ensuring consistent, reliable outputs across programs. • Build the processes, systems, and operating model required to scale programs efficiently without compromising quality. • Partner with Sales on customer discovery, solution design, scoping, and expansion opportunities. • Work closely with Product to translate partner needs and operational learnings into product requirements and roadmap priorities. • Build strong feedback loops across customers, model teams, Sales, Product, and Operations. • Own operational performance across quality, capacity, timelines, cost, throughput, and partner satisfaction. • Build and lead the program management and operations function as the business scales. Requirements What We’re Looking For • 7+ years in program management, product, operations, consulting, or similar roles. • Strong experience owning complex, cross-functional programs at scale. • Proven ability to build scalable operating processes and maintain a high quality bar. • Excellent product judgment and ability to turn ambiguous problems into structured execution. • Experience working with technical or enterprise customers. • Strong analytical, communication, and stakeholder-management skills. • High ownership and comfort operating in fast-moving environments. Strong plus: experience with AI, RLHF, model evaluation, data operations, frontier AI labs, or technical platforms. Success Looks Like • RL Gym programs scale rapidly while maintaining consistently high quality. • Partners trust us to deliver complex programs reliably. • Sales can confidently scope and grow RL Gym opportunities. • Product decisions are informed by real partner and operational needs. • Quality, capacity, throughput, and operational performance are measurable and continuously improving. About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description We are hiring an Engineering Manager to own and grow the Data Science group - the R&D team behind Alice's security and safety AI models and the data platform that powers them. You will lead a multidisciplinary team of ML engineers, data scientists, and software engineers who ship real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath. This is a hands-on people-leadership role: you set technical direction, are accountable for delivery and quality, and you build and grow the team. What your team owns The group is responsible for a large Python + Rust + PySpark monorepo (dozens of production services and shared libraries) spanning three connected domains: 1. Content-moderation inference at scale Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through an in-house ActiveServe framework over NVIDIA Triton and a Rust detection monolith, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models. 2. GenAI safety & red-teaming Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok) and forming Alice's leading edge into agentic-AI safety. 3. Data platform & MLOps An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection. What you'll do • Lead and grow the team - mentor ML engineers, data scientists, and software engineers, and own hiring, onboarding, 1:1s, career development, and performance. • Set direction and deliver - set technical direction and standards, turn company and product goals into a prioritized roadmap, and own the quality, reliability, and delivery of the systems above across parallel workstreams. • Champion excellence and partnership - stay hands-on to review designs and unblock the team, drive engineering excellence (testing, observability, CI/CD, on-call, cost/latency), keep the team at the state of the art in ML, LLMs, and GenAI safety, and partner with Product, Platform, and GenAI-safety stakeholders. Leadership competencies • People-first: builds trust, grows engineers, and creates a healthy, inclusive, high-ownership culture. • Outcome-oriented: drives clarity, sets priorities, and delivers under ambiguity without micromanaging. • Technical credibility: earns the team's respect through sound judgment on architecture and trade-offs. • Systems thinker: balances short-term delivery against long-term platform health, cost, and tech debt. Requirements What we're looking for (must-have) • Leadership & communication - a proven people manager of engineering or data-science teams (or a strong tech lead ready to step into formal management), with excellent communication and stakeholder management. • Hands-on engineering and ML at scale - strong production Python and software-engineering background with solid ML / data-science foundations (training, evaluation, deployment, monitoring), running services at scale on AWS and Kubernetes and large-scale data on Spark/PySpark and a lakehouse (Databricks or equivalent). • AI-augmented engineering - deep, daily fluency with an AI coding assistant (Claude Code, Cursor, or Codex), with the judgment to raise the whole team's leverage and set how these tools are used well. Nice to have • Experience with LLMs / GenAI in production - prompting, evaluation, guardrails, red-teaming, or agentic systems. • Experience with model serving on NVIDIA Triton (or similar) and the MLflow / Unity Catalog lifecycle. • Familiarity with Rust and Python/Rust interop (PyO3/maturin) for performance-critical paths. • Background in GenAI safety and security, content moderation, or another domain where model quality has real-world stakes. • Experience with event-driven architectures (Kafka, SQS) and Infrastructure-as-Code (Terraform). About Alice Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Description About Immunai: Immunai is an engineering-first platform company aiming to improve therapeutic decision-making throughout the drug discovery and development process. We are mapping the immune system at unprecedented scale and granularity and applying machine learning to this massive clinico-immune database, in order to generate novel insights into disease pathology for our partners - pharma companies and research institutes. We provide a comprehensive, end-to-end solution - from data generation and curation to therapeutics development, that continuously supports and validates the capabilities of our platform. As drug development is becoming increasingly inefficient, our ultimate goal is to help bring breakthrough medicines to patients as quickly and successfully as possible. Immunai is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. About the role: As the AI Product Director for the core components of Immunai's AI modeling framework, you will be responsible for defining and driving its product strategy, requirements, and roadmap, as well as lead a team of product managers. By leveraging Immunai's AI capabilities and vast data assets, you will spearhead the development of a product designed to empower internal users and external customers to address real-world challenges in drug discovery and development. Partnering tightly with R&D, you will ensure alignment between product strategy, scientific priorities and business objectives. You will advocate for your product both internally and externally, and ultimately be accountable for the value and impact your product delivers. Location: Ramat Gan, Israel (Hybrid model) What will you do? • Establish the long term vision and strategy to advance our AI-powered analytical platform to enable Immunai’s mid and long-term goals • Responsible for defining the comprehensive roadmap and related PRDs for the analytical platform, focusing on optimizing analytical workflows, tackling predictive challenges, and enhancing model interpretability, as well as partner with business, R&D and ops to realize the roadmap • Evangelize the product, creating understanding and alignment across key stakeholders • Drive value creation for analytical platform users by enabling them to leverage our data assets effectively, both internally and in external collaborations • Ensure that our analytical platform development is effectively enabling and accelerating our product, business, and therapeutic goals Requirements Required qualifications: • 5+ years of experience in product leadership roles - a must • Extensive experience in building, scaling, and managing AI-driven products throughout the product lifecycle (bonus if it’s in life sciences), with a strong track record of collaborating closely with data scientists and deep learning researchers — a must. • Exceptional ability to operate effectively at both the high level strategic and roadmap level, as well as at the detailed, hands on level when necessary in a highly technical space • BSc or higher degree in a technical or scientific discipline; specifically an academic degree in life sciences is a big plus • Exceptional written and verbal communication skills to technical and non-technical audiences Desired personal traits: • You want to make an impact on humankind • You prioritize “We” over “I” • You enjoy getting things done and striving for excellence • You collaborate effectively with people of diverse backgrounds and cultures • You have a growth mindset • You are candid, authentic, and transparent *Please note that when you apply for a position at Immunai, your application will be processed via our recruitment platform Comeet. You can read more about how we process personal data here: https://www.immunai.com/privacy-policy/

Description About Immunai: Immunai is an AI-driven platform company focused on improving drug discovery and development by decoding the human immune system. We combine large-scale single cell immune data, advanced machine learning, and strong engineering to help pharmaceutical and research partners make better, more informed decisions throughout the drug development process. Our long-term goal is to reduce drug development failure rates and help more effective medicines reach patients. We’re building this platform thoughtfully and collaboratively, bringing together expertise across biology, AI, engineering, and business. Immunai is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. About the role: We are looking for an AI Infrastructure Engineer to build the foundation that enables AI agents to reliably access and operate Immunai's internal systems, including code repositories, pipelines, datasets, and compute infrastructure. You will develop scalable, secure, and standardized infrastructure for agentic workflows, working closely with computational biology and engineering teams to continuously improve how AI supports research and development. This is a software infrastructure role, not a scientific modeling role, focused on building the systems that enable AI agents to operate effectively and safely. Location: Ramat Gan, Israel (Hybrid) What Will You Do? • Build Agent Infrastructure – Design and maintain infrastructure enabling AI agents to interact with internal systems, data, and compute. • Ensure Quality & Reliability – Establish testing, CI/CD, and evaluation standards for agentic workflows. • Implement Safety Controls – Build permissions, approvals, and audit mechanisms for autonomous agents. • Drive Continuous Improvement – Monitor agent behavior and usage to improve reliability and performance. • Scale Agentic Workflows – Support reliable autonomous and multi-agent operations across teams and users. • Optimize Efficiency – Manage performance, infrastructure costs, and compute utilization. • Collaborate Across Teams – Partner with research and engineering teams to enable AI-driven workflows. Requirements Required Qualifications: • BSc or MSc in Computer Science, Software Engineering, or a related field - a must • Hands-on experience with LLMs and agentic systems (agent frameworks, tool use, MCP, etc.), including production reliability - a must • 5+ years of hands-on experience in software engineering, designing modular, maintainable systems. • Proficiency in Python and cloud environments (GCP or AWS). • Experience with LLM/agent evaluation frameworks. • Experience with CI/CD, testing, and observability. Preferred Qualifications: • Experience with ML pipelines, experiment tracking, or MLOps tools (e.g., ClearML). • Background in biomedical or other data-intensive environments. Desired personal traits: • You want to make an impact on humankind • You prioritize “We” over “I” • You enjoy getting things done and striving for excellence • You collaborate effectively with people of diverse backgrounds and cultures • You have a growth mindset • You are candid, authentic, and transparent When you apply for a role at Immunai, we process your personal data (such as CV, contact details, and professional background) to assess your suitability for current and future roles. Your information may be shared internally with relevant employees and stored or processed by trusted service providers, some of whom may be located outside your country of residence. You can read more about how we process data here: https://www.immunai.com/privacy-policy/.

Description About Immunai: Immunai is an AI-driven platform company focused on improving drug discovery and development by decoding the human immune system. We combine large-scale single cell immune data, advanced machine learning, and strong engineering to help pharmaceutical and research partners make better, more informed decisions throughout the drug development process. Our long-term goal is to reduce drug development failure rates and help more effective medicines reach patients. We’re building this platform thoughtfully and collaboratively, bringing together expertise across biology, AI, engineering, and business. Immunai is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. About the role: We are looking for an ML Infra Engineer to play a central role in evaluating, and deploying Immunai’s AI models. You will own and evolve Immunai’s benchmarking and evaluation capabilities for foundation models and multimodal systems, while also working closely with modeling teams to support model development, iteration, and validation. This role sits at the intersection of software engineering, model understanding, and applied AI, with broad influence on how models are built, compared, and improved across the organization. This is NOT a core algorithmic research role - but rather, a role focused on building out the ML engineering infrastructure for the evaluation and deployment of models. Location: Ramat Gan, Israel (hybrid model) What will you do? • Own & Evolve Benchmarking – Design, build, and maintain Immunai’s benchmarking suite for foundation models and multimodal AI systems. • Define Core Abstractions – Create clean, extensible abstractions and APIs for datasets, tasks, models, metrics, and evaluation workflows. • Develop Metrics & Evaluations – Implement metrics that capture predictive performance, biological relevance, and multimodal alignment. • Support Model Development – Work closely with AI scientists and data scientists to integrate new models, tweak architectures, and enable rapid, fair iteration. • Bring in New Models & Baselines – Add external and internal models to benchmarks and ensure meaningful comparisons. • Explore Data When Needed – Dive into data and results to debug evaluations, understand model behavior, and unblock modeling work. • Enable Rigor & Reproducibility – Ensure evaluations are consistent, well-versioned, and trustworthy over time. Requirements Required qualifications: • BSc, MSc, or PhD in Computer Science, Software Engineering, or a related field • Strong software engineering skills with experience designing maintainable, modular systems • 5+ years hands-on, industry experience working with ML models and evaluation pipelines - a must • Proficiency in Python and modern ML ecosystems • Ability to read, modify, and debug deep learning models • Experience with benchmarks, metrics, or evaluation frameworks — preferred • Familiarity with foundation models or multimodal learning — preferred • Comfort navigating complex datasets and doing targeted exploratory analysis • Experience in biomedical or other data-intensive domains — a plus Desired personal traits: • You want to make an impact on humankind • You prioritize “We” over “I” • You enjoy getting things done and striving for excellence • You collaborate effectively with people of diverse backgrounds and cultures • You have a growth mindset • You are candid, authentic, and transparent When you apply for a role at Immunai, we process your personal data (such as CV, contact details, and professional background) to assess your suitability for current and future roles. Your information may be shared internally with relevant employees and stored or processed by trusted service providers, some of whom may be located outside your country of residence. You can read more about how we process data here: https://www.immunai.com/privacy-policy/.

Description About Immunai: Immunai is an AI-driven platform company focused on improving drug discovery and development by decoding the human immune system. We combine large-scale single cell immune data, advanced machine learning, and strong engineering to help pharmaceutical and research partners make better, more informed decisions throughout the drug development process. Our long-term goal is to reduce drug development failure rates and help more effective medicines reach patients. We’re building this platform thoughtfully and collaboratively, bringing together expertise across biology, AI, engineering, and business. Immunai is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. About the role: We’re seeking a Head of AI to lead research and engineering for multimodal foundation models and reasoning‑centric systems—from prototyping to reliable, secure, production deployment. The Head of AI will manage a group of deep learning researchers, computational biologists, immunologists and engineers and partner closely with product leaders and company leadership to convert cutting‑edge methods into high‑impact, mission‑critical capabilities. Location: Ramat Gan, Israel (hybrid model) What will you do? Group Leadership & Growth • Lead, mentor, and develop deep learning researchers, computational biologists, and machine learning engineers; set a high bar for scientific rigor, code quality, and delivery. • Establish clear ownership, role definitions, and growth paths; nurture a collaborative, low‑ego culture. Technical Strategy & Delivery • Own the AI roadmap and architecture; guide model design, evaluation, and productionization (training, serving, observability, safety). • Build scalable pipelines for transformers, multimodal fusion, and reasoning/agent frameworks; champion reproducibility, CI/CD for models, and cost‑efficient GPU/TPU utilization. Research Integration & Innovation • Stay current with the literature; run journal clubs and technical deep dives. • Evaluate, pilot, and integrate state‑of‑the‑art methods (advanced transformers, retrieval and tool‑use agents, causal/biological reasoning) into robust systems. Cross‑Functional Collaboration • Translate biological and clinical questions into tractable AI projects with measurable impact. • Communicate complex trade‑offs to peers and executives; align plans, risks, and milestones across functions. Mission & Impact • Prioritize initiatives that advance patient outcomes and create cumulative platform value. • Balance speed with scientific integrity, security, and compliance. Requirements Required qualifications: • People Leadership – 10+ years managing and developing technical teams (mix of deep learning researchers, data scientists, and ML engineers). • Engineering & AI Depth – Proven record architecting, building, and deploying large‑scale AI systems; strong software engineering foundations (Python, distributed systems, cloud, data platforms, MLOps). • Research Fluency – Up‑to‑date on modern AI; advantage for hands‑on work with transformers and reasoning/agent systems, or demonstrated ability to quickly partner with computational teams to connect the dots and make sound architectural choices. • Communication – Excellent written and verbal communication; able to align diverse stakeholders and influence at executive level, able to represent company’s AI vision externally. • Mission Orientation – Driven to improve patient outcomes; execution‑focused and committed to building durable value. • Collaboration & Low Ego – Highly cooperative, credits the team, and optimizes for collective success. Preferred Qualifications: • Strong advantage: Background in molecular biology/biomed, or success applying AI in complex, data‑rich, or regulated domains (e.g., computational biology, bioinformatics, immunology, healthcare). • Track record bridging research and product (publications, patents, or deployed systems with measurable impact). Desired personal traits: • You want to make an impact on humankind • You prioritize “We” over “I” • You enjoy getting things done and striving for excellence • You collaborate effectively with people of diverse backgrounds and cultures • You have a growth mindset • You are candid, authentic, and transparent When you apply for a role at Immunai, we process your personal data (such as CV, contact details, and professional background) to assess your suitability for current and future roles. Your information may be shared internally with relevant employees and stored or processed by trusted service providers, some of whom may be located outside your country of residence. You can read more about how we process data here: https://www.immunai.com/privacy-policy/.

Description About Immunai: Immunai is an AI-driven platform company focused on improving drug discovery and development by decoding the human immune system. We combine large-scale single cell immune data, advanced machine learning, and strong engineering to help pharmaceutical and research partners make better, more informed decisions throughout the drug development process. Our long-term goal is to reduce drug development failure rates and help more effective medicines reach patients. We’re building this platform thoughtfully and collaboratively, bringing together expertise across biology, AI, engineering, and business. Immunai is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. About the role: We are looking for an experienced Data Infrastructure Team Lead to lead our Data Infrastructure team. In this role, you will have formal people-management responsibility for a multidisciplinary team that builds, productionizes, and improves big data and analytics infrastructure. You will be responsible for team delivery, combining technical depth, agile leadership, and an AI-first mindset to help the team build reliable systems, improve how it works, and continuously raise its engineering standards. Location: Ramat Gan (Bursa area), Israel - Hybrid model What will you do? • Lead, manage, and develop a data infrastructure team of approximately 5-7 people. • Take responsibility for team delivery, including planning, prioritization, execution, and continuous improvement. • Lead agile ways of working, including scrum-based delivery practices. • Guide the design, productionization, and ongoing improvement of big data and analytics infrastructure. • Partner closely with cross-functional stakeholders across technical, scientific, and business interfaces. • Help the team adopt AI-first engineering practices, including automation and AI-assisted development workflows. • Drive improvements in team processes, technical quality, delivery predictability, and collaboration. • Support architectural and technical decisions across cloud infrastructure, data pipelines, orchestration, and production systems. • Create an environment where team members can grow, contribute, and improve together. Requirements Required qualifications: • 5+ years of experience leading data engineering, data infrastructure, or related engineering teams, of 5+ people - a must • Strong technical background in data infrastructure, cloud computing, and production systems - a must • Hands-on experience with data warehousing tools such as Snowflake or BigQuery, and data pipeline orchestration tools such as Dagster or Apache Airflow - a must • Experience developing in Python • Experience productionizing big data, analytics, or data platform infrastructure • Experience leading agile teams and working with scrum methodologies • Demonstrated ability to lead multidisciplinary teams and collaborate effectively across interfaces. • Track record of helping teams improve how they work, including process improvement, automation, technical quality, or delivery practices. • Strategic mindset with the ability to connect technical decisions to team and business goals. • Strong English communication skills, written and spoken, for working with an international and multilingual team. Preferred qualifications: • Experience working in a scientific domain. • Experience in biotech, immunology, life sciences, or another biology-adjacent domain. • Experience with agentic AI concepts, tools, or development patterns. • Experience helping software or data engineering teams transition toward AI-first development processes. • Familiarity with modern automation practices across engineering workflows, infrastructure, and delivery pipelines. Desired personal traits: • AI-first and automation-oriented mindset. • Strong people's leadership instincts and a genuine drive to help others improve. • Collaborative, clear, and thoughtful communication style. • High ownership, good energy, and comfort operating across disciplines. • Curious, pragmatic, and able to balance strategy with hands-on technical judgment. When you apply for a role at Immunai, we process your personal data (such as CV, contact details, and professional background) to assess your suitability for current and future roles. Your information may be shared internally with relevant employees and stored or processed by trusted service providers, some of whom may be located outside your country of residence. You can read more about how we process data here: https://www.immunai.com/privacy-policy/.