Senior ML Engineer (AI Research, Physical AI)

Location: Israel; remote in Europe and the United Kingdom

Company

Nebius builds a full-stack AI cloud platform covering data, model training, inference, infrastructure, and applied AI for developers and enterprises.

What you will do

• Design, implement, train, and evaluate large models and learning algorithms for robotic agents.
• Develop vision-language-action architectures connecting multimodal perception and language understanding with physical control.
• Investigate reinforcement learning, imitation learning, planning, guided generation, and action-trajectory search.
• Build datasets, capture methodologies, evaluation protocols, and data-quality pipelines for embodied learning.
• Develop simulation environments and conduct sim-to-real experiments on physical robotic platforms.
• Build research software and distributed training infrastructure, collaborate with research and engineering teams, and communicate findings through reports, open-source releases, demonstrations, and publications.

Requirements

• Senior-level experience in machine learning research and engineering, with deep expertise in at least one relevant area such as reinforcement learning, imitation learning, multimodal modeling, computer vision, robotics, planning, or control.
• Strong understanding of machine learning, reinforcement learning, or robot-learning theory and experience training transformer-based or multimodal foundation models.
• Substantial experience training large models across multiple computational nodes and designing statistically rigorous machine-learning experiments.
• Strong software engineering and algorithm-design skills; Python and JAX are used primarily.
• Ability to formulate research questions, design hypothesis-driven experiments, document findings, and collaborate across research and engineering disciplines.
• Applicants must be authorized to work in the country in which they apply and must provide proof of employment eligibility.

Nice to have

• Experience with real-world robots, robotic simulators, dexterous or mobile manipulation, whole-body control, or humanoid robotics.
• Experience with multimodal sensing, teleoperation, motion capture, wearable devices, or embodied-data collection.
• Experience with vision-language models, vision-language-action models, video models, world models, offline RL, PPO, reward modeling, or model-based RL.
• Familiarity with MuJoCo, Isaac Sim, Isaac Lab, PyBullet, ROS, FSDP, ZeRO, FlashAttention, quantization, or distributed checkpointing.
• PhD or equivalent practical experience, impactful publications or open-source contributions, and experience delivering robotic systems or large distributed training platforms.

Culture & Benefits

• Competitive compensation and career growth opportunities.
• Learning opportunities, flexibility, and ownership.
• Collaborative, innovative, and international environment.
• Opportunity to work on impactful AI projects with experienced research and engineering teams.
• Fast-moving environment focused on bold thinking, meaningful impact, trust, and shaping the future of AI.