AI Engineer 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. 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 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 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 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 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/.