AI Researcher jobs in Ramat Gan

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 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 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!

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!

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!

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!

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!