AI Researcher jobs in Israel

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!

We are making the future of Mobility come to life starting today. At Autofleet we support the world's largest vehicle fleet operators and transportation providers to optimize existing operations and seamlessly launch new, dynamic business models - driving efficient operations and maximizing utilization. We are looking for a talented AI Applied Researcher to help us develop advanced AI solutions that will empower impactful projects. The Data Science team leads the AI research and innovation efforts of the company, and is focused on pushing AI boundaries to enhance the product and optimize processes. What You’ll Do • Enterprise AI Agent Development: Designing, building, and deploying AI agents that automate and augment high-value workflows across business functions • Agentic Architecture & Orchestration: Owning the end-to-end agent stack - from tool use, memory management, and multi-step planning to human-in-the-loop escalation patterns, guardrails and audit trails. • Evaluation & Continuous Improvement: Defining agent evaluation frameworks that measure task completion, accuracy, hallucination rates, latency, and business impact - then iterating on agent behavior based on real usage data and stakeholder feedback. • Collaborate with engineering teams to design, build, and maintain production pipelines. • Stay up to date with the latest developments in AI, machine learning, and related fields, focusing on LLMs, exploring how emerging technologies can be applied to improve products and services. Requirements • Master’s/Ph.D. in Computer Science, Electrical Engineering, Machine Learning, information systems engineering, or related field. • 3+ years of applied AI/ML experience with a focus on Generative AI, LLMs, and deep learning. • Depth in agentic system design, retrieval-augmented generation, context and tool engineering, and rigorous evaluation. • Familiarity with AI frameworks (e.g., HuggingFace Transformers, LangChain, scikit-learn, PyTorch) • Familiarity with embedding models and vector databases • Exceptional problem-solving, creative thinking, and analytical skills. • Experience with voice AI technologies, speech recognition, or text-to-speech systems - strong advantage.

Join D-ID’s research team to develop the next generation of multimodal video diffusion models and bring cutting-edge research into real-time products used at global scale. About the Team Our research team is small, fast-moving, and highly impactful. We develop multimodal generative models for real-time video creation, turning cutting-edge research into scalable products. We are looking for researchers with deep expertise in generative video to join a team at the core of D-ID’s technology and help shape the future of generative AI. Why Work at D-ID? • Work on cutting-edge video-generation technology at the forefront of AI. • See your research become part of products used at global scale. • Own meaningful research and engineering challenges as part of a small, high-impact team. • Help solve complex problems in real-time video generation. • Build technology that brings the next generation of AI assistants to life. Role & Responsibilities • Research, develop, and evaluate generative AI models for video generation. • Design experiments and evaluation methods covering visual quality, temporal consistency, identity preservation, and performance. • Explore, validate, and implement innovative research ideas. • Own the full lifecycle from research and experimentation to production deployment. • Build models and systems that perform reliably at scale and in real time. • Collaborate closely with research, engineering, and product teams. You are • A curious researcher and dedicated self-learner. • Independent, proactive, and self-motivated. • A hands-on problem solver who is comfortable navigating open-ended research challenges. • A collaborative colleague who enjoys working across disciplines. • Comfortable working in a fast-paced, high-impact environment. Requirements • M.Sc., Ph.D., or equivalent industry experience. • Strong foundation in mathematics. • Expertise in image processing and computer vision. • A strong research track record in generative modeling, computer vision, or a related field. • Strong programming and software design skills. • Hands-on experience training video diffusion models. • Deep understanding of modern generative-model architectures and training methods. How to stand out in the crowd • Experience with multimodal models. • Experience optimizing model inference. • Trained large scale models over cloud infrastructure. About Us D-ID’s generative AI technology transforms customer experience, learning and development, sales, and marketing through video. Our platform enables creators to generate photorealistic digital presenters from text—dramatically reducing the cost and complexity of producing video content at scale and real time. Our customers include most of the Fortune 1000 companies, and organizations across financial services, automotive, technology, retail, entertainment, marketing, production, and social media. Founded in 2017 and backed by leading venture capital firms, D-ID makes its technology available through a self-service studio, API, and plug-ins. Our technology powers lifelike AI assistants and other interactive digital experiences. To date, more than 150 million videos have been created using D-ID’s technology, and over 200,000 developers have used our API. Now it’s your perfect time to join us!

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 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!

About Spacial: At Spacial, we’re redefining how the built world gets designed, engineered, and approved. With decades of combined experience across architecture, engineering, and construction, we’ve felt the industry’s pain points firsthand — slow approvals, endless redlines, and unclear requirements that derail even the best designs. We built Spacial to fix that. Our AI-powered platform automates structural and MEP engineering, transforming 2D plans into permit-ready, code-compliant drawings in days, not months. This isn’t just optimization. It’s a new standard built to give the AEC industry speed, clarity, and control from day one. By connecting architects, engineers, builders, and homeowners on a single platform, Spacial eliminates friction, streamlines approvals, and accelerates how ideas become reality. About the Role: We are seeking a highly innovative AI Researcher to conduct fundamental and applied research, pioneering the next generation of Generative AI solutions. This role is focused on the theoretical and practical advancement of Large Language Models (LLMs), agent-based architectures, and novel algorithms to create transformative solutions for the construction and design industry. You will be responsible for conducting rigorous experimentation, contributing to the research roadmap, and translating groundbreaking research insights into real-world applications. Responsibilities: • Conduct research, design, and implement novel Generative AI systems using LLMs, vision-language models, and advanced agentic frameworks (e.g., LangChain, LlamaIndex). • Develop and optimize cutting-edge algorithms and methodologies for LLM fine-tuning, retrieval-augmented generation (RAG) optimization, and advanced context engineering. • Perform rigorous experimentation and evaluation of novel GenAI-powered solutions to push the boundaries of model efficiency, performance, and robustness. • Collaborate closely with engineering teams to guide the technical translation of research prototypes into production-grade, high-impact AI products. • Contribute to the long-term AI research direction for the company, staying at the forefront of emerging techniques and academic breakthroughs. • Develop and implement computer vision algorithms that significantly enhance the performance and functionality of our product. Qualifications: • Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related quantitative field (Mathematics, Physics, Electrical Engineering, Computational Biology, etc.). • Minimum of 5+ years of combined experience in AI/ML research and development, with a deep focus on Generative AI, LLMs, and Deep Learning. Required Skills: • A strong, demonstrable track record of research impact, including a history of publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, AAAI, CVPR, ICCV, ACL) or significant open-source contributions/patents in AI/ML. • Expertise in the theory and practical application of LLM fine-tuning, advanced RAG architectures, and complex agentic systems. • Exceptional proficiency in Python and deep expertise with major ML/Deep Learning frameworks (PyTorch, TensorFlow, Hugging Face Transformers). • Strong foundation in software engineering and algorithm development, with experience working with large datasets. • Exceptional problem-solving, creative thinking, and analytical skills. Preferred Skills: • Experience with multimodal AI (vision-language models, diffusion models). • Familiarity with the challenges and real-time considerations for managing AI workloads in a production environment. • Knowledge of parallel computing and optimization techniques for large-scale model training and inference. Why Join Us? Opportunity to define and shape the future of AI-powered solutions at the critical intersection of fundamental research and real-world product impact. Work directly on cutting-edge GenAI technologies with the potential for a significant global impact in the construction and design industry. A collaborative, innovative startup environment with direct access to experienced founders. Flexible hybrid work arrangement. If you are a pioneering researcher passionate about pushing the theoretical and practical boundaries of Generative AI, we would love to hear from you.

Develop cutting-edge AI methods to design and optimize antibodies, advancing therapeutic programs and Scala's protein design platform. Scala Biodesign is a biotech startup pioneering computational protein design platforms that power the next generation of biologics and industrial enzymes. Built on over a decade of research at the Weizmann Institute of Science, Scala's technology is trusted by ten of the world's top twenty pharmaceutical companies and by global leaders in chemical production. Our platform has been used to design antibodies, complex drug targets, and enzymes, including a malaria vaccine candidate currently in Phase II clinical trials. Our platforms fuse physics-based modeling, AI, and evolutionary data and have been shown in numerous cases to optimize key properties such as potency, manufacturability, and stability under industrially relevant conditions, delivering major performance gains in a single design round and significantly reducing experimental costs. We are looking for an Applied AI Researcher to join our multidisciplinary Antibody team composed of computational biologists, mathematicians and protein designers. In this role, you will drive the development and application of advanced machine learning methods to design and optimize antibodies, contributing directly to cutting-edge therapeutic programs and to the evolution of Scala's computational platform. Responsibilities: - Develop and apply cutting-edge AI/ML methods for antibody design, including generative modeling and protein language models - Design and implement algorithms for multi-objective optimization of antibodies (e.g., affinity, specificity, stability, developability) - Build tools to support iterative design cycles, combining computational predictions with experimental feedback - Collaborate closely with experimental and computational teams to translate models into impactful design outcomes - Stay current with advances in machine learning, structural biology, and AI-driven protein design Requirements: - Ph.D. in Computer science, Physics, Mathematics, computational biology or related field with 2+ years of industry experience, or an M.Sc. with 5+ years of relevant industry experience. - Strong proficiency in Python and PyTorch, with hands-on experience training and fine-tuning deep learning models on GPUs. - Strong algorithmic thinking and problem-solving skills, with the ability to translate theoretical ideas into practical solutions. - Strong communication and collaboration skills. - Fast learner and proven ability to master new domains. - Advantage: Proven experience developing and applying machine learning models to biological or chemical data. Experience with antibody modeling and design (sequence, structure, or interaction prediction). - Advantage: experience profiling and optimizing GPU workloads.

Applied AI Scientist We're hiring an exceptional Applied AI Scientist to join our team! In this role, you'll design and deploy enterprise-grade AI agents that drive real business impact across Tipalti's core functions - Sales, Marketing, Customer Success, Customer Support, Risk, and Finance. You'll be building agentic systems that execute complex, multi-step business workflows end-to-end. The ideal candidate is a hands-on builder with deep AI expertise who thrives on turning complex business problems into reliable, production-grade AI systems. Why join Tipalti? Tipalti is the AI-powered platform for finance automation, elevating how finance teams operate in the global economy. We empower our customers to scale faster and smarter by removing the complexities of doing global business and accelerating their finance operations efficiency. Our platform provides a comprehensive suite of finance automation solutions designed for mid-market businesses across accounts payable, global payouts, procurement, employee expenses, corporate cards, supplier management, tax compliance, and treasury. Tipalti partners with leading financial institutions such as Citi, Wells Fargo, J.P. Morgan, and Visa, enabling over 5,000 global companies to efficiently and securely pay millions of suppliers and payees across 200+ countries and territories, in 120 currencies. At Tipalti, we pride ourselves on our collaborative culture, the quality of our product and the capabilities of our people. Tipaltians are passionate about the work they do, and keen to get the job done. Tipalti offers competitive benefits, a flexible workplace, and an environment where diverse individuals can thrive and make an impact. Our culture ensures everyone checks their egos at the door and stands ready to reach for success together. Founded in Israel in 2010, Tipalti is a global business headquartered in the San Francisco Bay Area (Foster City) with offices in Tel Aviv, Plano, Toronto, Vancouver, London, Amsterdam, Tbilisi and Medellin. In This Role, You Will Be Responsible For • Enterprise AI Agent Development: Designing, building, and deploying AI agents that automate and augment high-value workflows across business functions - including lead qualification and outreach (Sales & Marketing), churn reduction(Customer Success), and Ticket deflection (Support). • Agentic Architecture & Orchestration: Owning the end-to-end agent stack - from tool use, memory management, and multi-step planning to human-in-the-loop escalation patterns, guardrails, and audit trails suited for an enterprise fintech environment. • Knowledge Systems: Building and maintaining RAG pipelines that ground agents in Tipalti's proprietary knowledge - product documentation, customer data, financial records, and internal playbooks - using vector databases, hybrid search, and re-ranking. • Evaluation & Continuous Improvement: Defining agent evaluation frameworks that measure task completion, accuracy, hallucination rates, latency, and business impact - then iterating on agent behavior based on real usage data and stakeholder feedback. • Cross-Functional Collaboration: Partnering with stakeholders across the company to define agentic use cases, translate business requirements into technical features, and deliver products that create measurable ROI - from data preprocessing through product deployment. About You • Bachelor’s degree in Computer Science, Engineering or a related field, with focus on AI. • 2.5-5 years of hands-on experience in AI/ML engineering, ideally within the fintech industry or a related sector, with a track record of deploying AI agents or agentic workflows in production • Proficiency in Python and SQL for data manipulation, analysis, and AI system building. • Deep experience with LLMs and prompt engineering, including systematic evaluation of enterprise AI solutions • Experience with building RAG systems and Conversational chatbots - Advantage • Experience with cloud platforms (AWS preferred) for deployment and AI-Ops practices. • Analytical Mindset: You bring strong statistical reasoning and business acumen, with the ability to critically assess AI solutions and their real-world impact. • Project Management: You can manage and prioritize multiple tasks, balancing short-term and long-term goals to deliver timely, high-impact results. Why join Tipalti? Tipalti is the AI-powered platform for finance automation, elevating how finance teams operate in the global economy. We empower our customers to scale faster and smarter by removing the complexities of doing global business and accelerating their finance operations efficiency. Our platform provides a comprehensive suite of finance automation solutions designed for mid-market businesses across accounts payable, global payouts, procurement, employee expenses, corporate cards, supplier management, tax compliance, and treasury. Tipalti partners with leading financial institutions such as Citi, Wells Fargo, J.P. Morgan, and Visa, enabling over 5,000 global companies to efficiently and securely pay millions of suppliers and payees across 200+ countries and territories, in 120 currencies. At Tipalti, we pride ourselves on our collaborative culture, the quality of our product and the capabilities of our people. Tipaltians are passionate about the work they do, and keen to get the job done. Tipalti offers competitive benefits, a flexible workplace, career coaching, and an environment where diverse individuals can thrive and make an impact. Our culture ensures everyone checks their egos at the door and stands ready to reach for success together. Founded in Israel in 2010, Tipalti is a global business headquartered in the San Francisco Bay Area (Foster City) with offices in Tel Aviv, Plano, Toronto, Vancouver, London, Amsterdam, Tbilisi and Medellin. Our tech teams are the engine behind our business. Tipalti’s tech ecosystem is extremely rich and we continually add new features to our products, ensuring that we respond to our clients’ needs at scale. Our tech teams retain a fast-paced, start-up vibe that encourages innovation and critical thinking. At Tipalti, you’ll have the opportunity to work with a diverse, global team of engineers, developers and product leaders who are collectively building the future of our best-in-class product suite as we transform financial operations for the future. About Our Tel Aviv Offices: Hybrid Work Model - Tipalti offers a hybrid work model, with two days per week working from home and three days per week from our beautiful offices in North Tel Aviv. Shuttle Services - Shuttle services are available from the nearest train station and across Tel Aviv to the office. Parking - Parking is available for all employees. Snacks & Treats - Enjoy a selection of snacks and treats available on every floor. #LI-hybrid Our Mission Our mission is to elevate how finance teams operate in the global economy. We empower our customers to scale faster and smarter by removing the complexities of doing global business and accelerating their finance operations efficiency. We are the AI-powered platform that automates finance. Tipalti is fueled by a commitment to our customers and a desire to build lasting connections. Our client portfolio includes high-velocity businesses such as Amazon Twitch, GoDaddy, Roku, WordPress.com, and ZipRecruiter. We work hard for our 99% customer retention rate which is built on trust, reliability and innovation. Tipalti means we handled it" - a mission to which we are constantly committed. Accommodations Tipalti champions inclusive teams, in which every voice counts. We are committed to recruiting diverse candidates with varied personal experiences and abilities. We welcome applications from candidates belonging to historically underrepresented or disadvantaged groups, and maintain an equitable Talent Acquisition process that is free from discrimination. As an equal opportunities employer, Tipalti complies with employment and human rights laws across the various jurisdictions in which we operate. Should you require reasonable adjustments or accommodations during the recruitment process, including access to alternate formats of materials, meeting spaces, or other accommodations that could better enable your full participation, please reach out to [email protected] for assistance. AI Use We may use artificial intelligence and automated systems (collectively "AI") to screen, assess, and select candidates during our recruitment process. This includes resume screening, skills assessment, and candidate matching. You have the right to request human review of any automated decision. For more information about how we collect and use personal data and information during recruitment, please refer to our Job Candidate Privacy Notice. For additional questions about our use of AI during our recruitment process, you can contact [email protected]. Privacy We are committed to protecting the privacy interests of job applicants and candidates. For more information about our privacy practices during our Talent Acquisition process, please refer to our Job Candidate Privacy Notice below: Job Candidate Privacy Notice | Tipalti www.tipalti.com/privacy/job-candidate-privacy-notice/

The CTO Group is a small, elite team focused on the decision-making module of Mobileye's autonomous driving system. Our mission is to solve some of the hardest problems in autonomous driving by combining the full spectrum of modern computer science - from rigorous mathematical modeling, optimization, and algorithm design to deep learning. The team develops the driving policies that translate system-wide inputs into safe, efficient, and human-like driving behavior. Our work spans classical algorithms, optimization under constraints, probabilistic reasoning, reinforcement & supervised learning, all grounded in real-world driving data. The group is comprised of top researchers and developers and is led by world-class scientists, offering a unique opportunity to work closely with the CTO, shape the future of autonomous driving, and influence the company's technical direction. What will your job look like: • Research and develop innovative algorithms for autonomous driving planning and decision-making. • Model complex real-world driving scenarios and formulate them as optimization, learning, or algorithmic problems. • Apply a broad range of techniques - from mathematical modeling and classical algorithms to machine learning and deep learning - to improve driving policies. • Analyze large-scale driving data to gain insights, validate hypotheses, and improve system robustness and performance. • Design and implement high-quality production-ready code in a research-driven environment. • Read, evaluate, and build upon state-of-the-art scientific literature in algorithms, AI, and machine learning. All you need is: • 5+ years of experience in algorithm development, machine learning, or AI research. • Outstanding MSc in Computer Science, Computer Engineering, Mathematics, Physics, or a related quantitative field. • PhD - an advantage. • Strong programming skills in C++ and Python. • Outstanding mathematical and analytical abilities. • Passion for solving challenging research problems and the ability to quickly learn from scientific literature. Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!

Mobileye is a pioneering force in the autonomous vehicles (AV) industry. Join us to build the brain behind the car — a large-scale, multi-task neural network that powers the core of Mobileye’s autonomous stack. You’ll design and train cutting-edge deep learning models tailored for our custom EyeQ chip, tackling end-to-end challenges and deploying real-world solutions. From novel architectures and advanced training techniques to performance tuning under tight constraints, you’ll work closely with software and hardware teams to turn research into high-impact, production-ready systems. If you’re a brilliant, hands-on researcher with a passion for shaping the future — this is your launchpad. Our team is at the forefront of Mobileye’s most advanced AI efforts. As a central hub for deep learning innovation, we’re trusted with designing the core neural network architecture that powers the company’s flagship products. If you’re seeking a high-impact role among top-tier researchers and developers — this is the place to be. All you need is: • PhD in Computer Science or a related discipline (exceptional MSc candidates will be considered). • 4+ years of hands-on experience developing deep learning algorithms in Python. • Experience building end-to-end DL pipelines: data preparation, training, evaluation, and deployment. • Proficiency in at least one deep learning framework (e.g., TensorFlow, PyTorch). • Excellent problem-solving skills and a research-oriented mindset. Advantages: • Industry experience in DL or software development. • Familiarity with hardware-aware model optimization. • Experience with cloud platforms (e.g., AWS), Docker, and Linux environments. • Publications or contributions in the fields of deep learning, neural architecture search, knowledge distillation or multi-task learning. Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!

How do you generate a driving scene that stays consistent over time, across cameras, and under controlled changes? These are exactly the types of problems we solve. Mobileye’s Visual GenAI team develops image and video generation capabilities for autonomous driving, with a growing focus on video and world models. We’re looking for a hands-on researcher to help shape our research direction and turn ideas into working capabilities, with access to large proprietary driving datasets and close collaboration with the teams developing Mobileye’s next generation of autonomous driving technology. What will your job look like: • Research and develop image and video generation models, with a focus on temporal consistency, multiview coherence, and controllable driving-scene generation. • Explore and adapt state-of-the-art world models for autonomous driving, helping shape the team’s research and technical direction. • Own the full development lifecycle, from research and experimentation to model training, evaluation, and integration into real-world workflows. • Build scalable, clean Python and PyTorch implementations for training and evaluating generative models. • Collaborate with AI researchers and perception teams to develop generative capabilities for training and evaluating autonomous driving systems. All you need is: • M.Sc. or Ph.D. in Computer Science, Electrical Engineering, or a related field. • 3+ years of industry experience in Deep Learning and Computer Vision, preferably working on generative AI or foundation models. • A hands-on approach and the ability to turn research ideas into working solutions. Nice to have: • Hands-on experience training image or video generation models, including diffusion and flow-based models. • Experience with world models, multiview generation, 3D scene representations, or temporal modeling. • Experience training or adapting foundation models, including Vision-Language Models (VLMs) and other multimodal models. • Experience with distributed training and large-scale datasets. • Publications in top-tier conferences such as CVPR, NeurIPS, ICCV, or ECCV. Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!

How do you generate a driving scene that stays consistent over time, across cameras, and under controlled changes? These are exactly the types of problems we solve. Mobileye’s Visual GenAI team develops image and video generation capabilities for autonomous driving, with a growing focus on video and world models. We’re looking for a hands-on researcher to help shape our research direction and turn ideas into working capabilities, with access to large proprietary driving datasets and close collaboration with the teams developing Mobileye’s next generation of autonomous driving technology. What will your job look like: • Research and develop image and video generation models, with a focus on temporal consistency, multiview coherence, and controllable driving-scene generation. • Explore and adapt state-of-the-art world models for autonomous driving, helping shape the team’s research and technical direction. • Own the full development lifecycle, from research and experimentation to model training, evaluation, and integration into real-world workflows. • Build scalable, clean Python and PyTorch implementations for training and evaluating generative models. • Collaborate with AI researchers and perception teams to develop generative capabilities for training and evaluating autonomous driving systems. All you need is: • Ph.D. in Computer Science, Electrical Engineering, or a related field. Exceptional M.Sc. candidates with a specialization in Gen AI will also be considered. • 5+ years of industry experience in Deep Learning and Computer Vision, preferably working on generative AI or foundation models. • A hands-on approach and the ability to turn research ideas into working solutions. Nice to have: • Hands-on experience training image or video generation models, including diffusion and flow-based models. • Experience with world models, multiview generation, 3D scene representations, or temporal modeling. • Experience training or adapting foundation models, including Vision-Language Models (VLMs) and other multimodal models. • Experience with distributed training and large-scale datasets. • Publications in top-tier conferences such as CVPR, NeurIPS, ICCV, or ECCV. Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!

Build the intelligence behind the next driving decision. We’re building a reinforcement-learning driving planner for complex, interactive road scenarios. We’re looking for a researcher to help take it from simulation to real vehicles. You’ll develop policy models, rewards, and training methods. You’ll define how driving behavior is evaluated, analyze failures in closed loop, and validate improvements on the road. You’ll work in a small team at Mobileye and collaborate with control and other algorithm teams. This is a high-impact role with direct influence on a core part of Mobileye’s driving technology and its future products. What will your job look like? • Research and develop reinforcement-learning planning algorithms, including policy architectures, reward design, training objectives, and optimization methods. • Train and evaluate RL policies for difficult, interactive driving scenarios, building on the existing learning-based planner and complementary classical components. • Develop evaluation methods and relevant metrics for safety, progress, comfort, and interaction quality, and use them to guide experiments and analyze failures. • Build simulation-based training and closed-loop evaluation workflows. • Turn research ideas into reliable components of the driving stack. All you need is: • M.Sc. or Ph.D. in Computer Science, Electrical Engineering, or a related field. • 3+ years of hands-on industry experience in deep learning, including designing and training neural networks. • Hands-on reinforcement-learning experience through research or practical application. • Experience in autonomous driving, robotics, motion planning, simulation, or closed-loop evaluation- an advantage Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!

Mobileye is a pioneering force in the autonomous vehicles (AV) industry. Join us to build the brain behind the car — a large-scale, multi-task neural network that powers the core of Mobileye’s autonomous stack. You’ll design and train cutting-edge deep learning models tailored for our custom EyeQ chip, tackling end-to-end challenges and deploying real-world solutions. From novel architectures and advanced training techniques to performance tuning under tight constraints, you’ll work closely with software and hardware teams to turn research into high-impact, production-ready systems. If you’re a brilliant, hands-on researcher with a passion for shaping the future — this is your launchpad. Our team is at the forefront of Mobileye’s most advanced AI efforts. As a central hub for deep learning innovation, we’re trusted with designing the core neural network architecture that powers the company’s flagship products. If you’re seeking a high-impact role among top-tier researchers and developers — this is the place to be. All you need is: • PhD in Computer Science or a related discipline (exceptional MSc candidates will be considered). • 4+ years of hands-on experience developing deep learning algorithms in Python. • Experience building end-to-end DL pipelines: data preparation, training, evaluation, and deployment. • Proficiency in at least one deep learning framework (e.g., TensorFlow, PyTorch). • Excellent problem-solving skills and a research-oriented mindset. Advantages: • Industry experience in DL or software development. • Familiarity with hardware-aware model optimization. • Experience with cloud platforms (e.g., AWS), Docker, and Linux environments. • Publications or contributions in the fields of deep learning, neural architecture search, knowledge distillation or multi-task learning. Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!

Build the intelligence behind the next driving decision. We’re building a reinforcement-learning driving planner for complex, interactive road scenarios. We’re looking for a researcher to help take it from simulation to real vehicles. You’ll develop policy models, rewards, and training methods. You’ll define how driving behavior is evaluated, analyze failures in closed loop, and validate improvements on the road. You’ll work in a small team at Mobileye and collaborate with control and other algorithm teams. This is a high-impact role with direct influence on a core part of Mobileye’s driving technology and its future products. What will your job look like? • Research and develop reinforcement-learning planning algorithms, including policy architectures, reward design, training objectives, and optimization methods. • Train and evaluate RL policies for difficult, interactive driving scenarios, building on the existing learning-based planner and complementary classical components. • Develop evaluation methods and relevant metrics for safety, progress, comfort, and interaction quality, and use them to guide experiments and analyze failures. • Build simulation-based training and closed-loop evaluation workflows. • Turn research ideas into reliable components of the driving stack. All you need is: • M.Sc. or Ph.D. in Computer Science, Electrical Engineering, or a related field. • 3+ years of hands-on industry experience in deep learning, including designing and training neural networks. • Hands-on reinforcement-learning experience through research or practical application. • Experience in autonomous driving, robotics, motion planning, simulation, or closed-loop evaluation- an advantage Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!

The CTO Group is a small, elite team focused on the decision-making module of Mobileye's autonomous driving system. Our mission is to solve some of the hardest problems in autonomous driving by combining the full spectrum of modern computer science - from rigorous mathematical modeling, optimization, and algorithm design to deep learning. The team develops the driving policies that translate system-wide inputs into safe, efficient, and human-like driving behavior. Our work spans classical algorithms, optimization under constraints, probabilistic reasoning, reinforcement & supervised learning, all grounded in real-world driving data. The group is comprised of top researchers and developers and is led by world-class scientists, offering a unique opportunity to work closely with the CTO, shape the future of autonomous driving, and influence the company's technical direction. What will your job look like: • Research and develop innovative algorithms for autonomous driving planning and decision-making. • Model complex real-world driving scenarios and formulate them as optimization, learning, or algorithmic problems. • Apply a broad range of techniques - from mathematical modeling and classical algorithms to machine learning and deep learning - to improve driving policies. • Analyze large-scale driving data to gain insights, validate hypotheses, and improve system robustness and performance. • Design and implement high-quality production-ready code in a research-driven environment. • Read, evaluate, and build upon state-of-the-art scientific literature in algorithms, AI, and machine learning. All you need is: • 5+ years of experience in algorithm development, machine learning, or AI research. • Outstanding MSc in Computer Science, Computer Engineering, Mathematics, Physics, or a related quantitative field. • PhD - an advantage. • Strong programming skills in C++ and Python. • Outstanding mathematical and analytical abilities. • Passion for solving challenging research problems and the ability to quickly learn from scientific literature. Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!

Build the intelligence behind the next driving decision. We’re building a reinforcement-learning driving planner for complex, interactive road scenarios. We’re looking for a researcher to help take it from simulation to real vehicles. You’ll develop policy models, rewards, and training methods. You’ll define how driving behavior is evaluated, analyze failures in closed loop, and validate improvements on the road. You’ll work in a small team at Mobileye and collaborate with control and other algorithm teams. This is a high-impact role with direct influence on a core part of Mobileye’s driving technology and its future products. What will your job look like? • Research and develop reinforcement-learning planning algorithms, including policy architectures, reward design, training objectives, and optimization methods. • Train and evaluate RL policies for difficult, interactive driving scenarios, building on the existing learning-based planner and complementary classical components. • Develop evaluation methods and relevant metrics for safety, progress, comfort, and interaction quality, and use them to guide experiments and analyze failures. • Build simulation-based training and closed-loop evaluation workflows. • Turn research ideas into reliable components of the driving stack. All you need is: • M.Sc. or Ph.D. in Computer Science, Electrical Engineering, or a related field. • 3+ years of hands-on industry experience in deep learning, including designing and training neural networks. • Hands-on reinforcement-learning experience through research or practical application. • Experience in autonomous driving, robotics, motion planning, simulation, or closed-loop evaluation- an advantage Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!

Our Mission At Palo Alto Networks®, we’re united by a shared mission—to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you’re ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you’re in the right place. Who We Are In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values: Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real-world problems and ideating beside the best and the brightest, we invite you to join us! We believe collaboration thrives in person. That’s why most of our teams work from the office full time, with flexibility when it’s needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes. Job Summary Shape the future of AI-powered software engineering at Palo Alto Networks. As part of our AI Transformation group, you’ll join a multidisciplinary group building state-of-the-art, agentic AI systems that directly elevate the productivity of thousands of internal engineers. We build internal AI infrastructure, frameworks, agents, harnesses, methodologies, and adoption programs powered by cutting-edge LLMs. Whether you are researching novel AI paradigms, architecting platform infrastructure, engineering end-to-end agentic tools, or driving developer enablement, your work will redefine how software at Palo Alto Networks gets built at scale. This is an applied AI research role, where you will be inventing new technology, bringing the latest innovations into our agentic software development practices, and responsible for co-building systems that get deployed to our entire, global Cortex engineering organization. Key Responsibilities • Stay at the forefront of AI advancements in agentic coding and integrate state-of-the-art techniques. • Build functional Proof of Concepts (PoCs) and architectural skeletons to evaluate new AI techniques before scaling. • Critically audit and challenge internal AI solutions, benchmarks, and architectures to prevent technological obsolescence. • Design rigorous testing methodologies to assess model accuracy, latency, cost efficiency, and drift across solutions. • Research and design advanced knowledge-retrieval architectures, including standard and graph-based RAG and hybrid contextual search. • Collaborate with core AI engineering to transition validated prototypes into reusable components for our engineering workforce. • Coordinate closely with the US Foundational AI Research team via flexible evening Israel / morning US meetings. Qualifications Required Qualifications • Minimum of 5 years of experience in Applied AI research, Data Science, or Machine Learning engineering. • Deep, hands-on experience over the past 12+ months developing leading-edge agentic coding practices. • Proficiency in Python and/or Typescript, with deep familiarity in Agentic AI orchestration libraries like LangChain, LlamaIndex, or LangGraph. • Experience with automated statistical evaluation to track model drift, latency, costs, and hallucination rates. • Hands-on experience developing advanced RAG pipelines, GraphRAG frameworks, and interacting with Graph Databases or vector indices. • Practical experience with autonomous agent protocols, multi-agent communication layers, or Model Context Protocol (MCP). Preferred Qualifications • Understanding of secure data pipelines, including Role-Based Access Control (RBAC) and prompt injection mitigation. • Hands-on production experience utilizing Google Cloud Platform (GCP) services and infrastructure. • Strong knowledge of data privacy and security considerations specific to AI and ML systems. • Post-graduate work or equivalent advanced degree in Artificial Intelligence, Data Science, or a related field. Our Commitment We’re trailblazers that dream big, take risks, and challenge cybersecurity’s status quo. It’s simple: we can’t accomplish our mission without diverse teams innovating, together. We are committed to providing reasonable accommodations for all qualified individuals with a disability. If you require assistance or accommodation due to a disability or special need, please contact us at [email protected]. Palo Alto Networks is an equal opportunity employer. We celebrate diversity in our workplace, and all qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or other legally protected characteristics. All your information will be kept confidential according to EEO guidelines. Is role eligible for Immigration Sponsorship? No. Please note that we will not sponsor applicants for work visas for this position.