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 d