Senior ML Engineer (AI Research, Physical AI)

About Nebius:

Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.

Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.

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 m …