Senior ML Engineer (AI Research, Physical AI)

The role

This role is for Nebius AI R&D, a team focused on applied research in AI. Our Physical AI research aims to build intelligent agents that can perceive, reason, and act in the physical world. Research areas include:

Vision-language-action models for general-purpose robotic control

Reinforcement and imitation learning from human demonstrations, simulation, and real-world experience

Scalable collection, generation, and curation of multimodal embodied data

Simulation, world models, and sim-to-real transfer

Multimodal sensing, including vision, touch, force, and proprioception

You will modify large foundation models and learning algorithms for robotic agents, prototype new capabilities in simulation, and validate promising approaches on real-world systems. The results will often lead to collaboration with adjacent research, infrastructure, and engineering teams, where findings are scaled and applied in practice.

We are currently looking for senior- and staff-level ML engineers to work on research in areas such as:

Vision-language-action models and multimodal foundation models for robotics

Reinforcement learning, imitation learning, and learning from demonstrations

Scalable acquisition and generation of human, robot, and simulated interaction data

World models, planning, and model-based control

Sim-to-real transfer, domain adaptation, and robust policy evaluation

Dexterous manipulation, whole-body control, and general-purpose robotic agents

Some examples of what your responsibilities might include are:

Designing, implementing, training, and evaluating large models and learning algorithms for robotic agents

Developing vision-language-action architectures that connect multimodal perception and language understanding with physical control

Investigating reinforcement learning and imitation learning methods for sparse, delayed, or difficult-to-verify objectives

Building scalable methods for incorporating demonstrations, teleoperation data, video, simulation trajectories, and autonomous robot experience into foundation models

Designing capture methodologies, datasets, evaluation protocols, and data-quality pipelines for embodied learning

Developing simulation environments and conducting sim-to-real experiments on physical robotic platforms

Exploring planning, guided generation, and search over action trajectories

Prototyping new capabilities in areas such as dexterous manipulation, mobile manipulation, and whole-body control

Writing robust research software and distributed training infrastructure that enable rapid experimentation

Collaborating with research and engineering teams to translate promising ideas into reliable real-world systems

Communicating results through technical reports, open-source releases, demonstrations, and research publications

We expect you to have:

A profound understanding of the theoretical foundations of machine learning, reinforcement learning, or robot learning

Deep expertise in at least one relevant area, such as reinforcement learning, imitation learning, multimodal generative modeling, computer vision, robotics, planning, or control

Experience training and evaluating modern deep learning models, including transformer-based or multimodal foundation models

Substantial experience training large models across multiple computational nodes

Strong software engineering and algorithm-design skills; we primarily use Python

Deep experience with a modern deep learning framework; we primarily use JAX

Experience designing, executing, and analyzing machine learning experiments with appropriate statistical rigor

Ability to formulate meaningful research questions, design experiments that test clear hypotheses, and draw defensible conclusions

Experience implementing research ideas and iterating quickly across modeling, data, infrastructure, and evaluation

Strong communication and leadership abilities, including the ability to collaborate across research and engineering disciplines

Ability to document research findings clearly and contribute to technical reports or research publications

Nice to have:

Experience working with real-world robots and robotic simulation environments

Experience with dexterous manipulation, whole-arm manipulation, mobile manipulation, or humanoid robotics

Experience with multimodal sensing, including tactile, force-torque, depth, and proprioceptive signals

Experience collecting human demonstrations through teleoperation, motion capture, wearable devices, or observation

Experience developing or post-training vision-language models, vision-language-action models, or video and world models

Experience with deep reinforcement learning techniques such as offline RL, actor-critic methods, PPO, reward modeling, preference learning, or model-based RL

Familiarity with robotics tools and simulators such as MuJoCo, Isaac Sim, Isaac Lab, PyBullet, ROS, or equivalent systems

Knowledge of scalable training techniques such as FSDP or ZeRO, FlashAttention, mixed-precision training, quantization, and distributed checkpointing

A PhD in Computer Science, Robotics, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience

A track record of impactful publications, open-source contributions, or deployed robotic systems

Experience engineering large distributed data-processing, simulation, or model-training systems

A record of building and delivering products or research prototypes in a dynamic, startup-like environment

Passion for moving research from controlled experiments to capable, reliable real-world robotic systems

Excellent command of English, with strong technical writing, presentation, and communication skills

Proficiency in contemporary software engineering practices, including version control, testing, code review, and CI/CD