About the role

<div class="content-intro">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.</div><h3 id="The-role" data-local-id="56e0c79db6b1" data-renderer-start-pos="1">The role</h3> 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: <ul class="ak-ul" data-local-id="b201d55e-9630-42b8-a3e6-6366f9d7aa7b" data-indent-level="1"> <li> Vision-language-action models for general-purpose robotic control </li> <li> Reinforcement and imitation learning from human demonstrations, simulation, and real-world experience </li> <li> Scalable collection, generation, and curation of multimodal embodied data </li> <li> Simulation, world models, and sim-to-real transfer </li> <li> Multimodal sensing, including vision, touch, force, and proprioception </li> </ul> 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: <ul class="ak-ul" data-local-id="2bdd11b4-ceac-4819-8cb0-f5d0ed796404" data-indent-level="1"> <li> Vision-language-action models and multimodal foundation models for robotics </li> <li> Reinforcement learning, imitation learning, and learning from demonstrations </li> <li> Scalable acquisition and generation of human, robot, and simulated interaction data </li> <li> World models, planning, and model-based control </li> <li> Sim-to-real transfer, domain adaptation, and robust policy evaluation </li> <li> Dexterous manipulation, whole-body control, and general-purpose robotic agents </li> </ul> Some examples of what your responsibilities might include are: <ul class="ak-ul" data-local-id="1a3b57bf-356e-484f-8c7d-10071076da9e" data-indent-level="1"> <li> Designing, implementing, training, and evaluating large models and learning algorithms for robotic agents </li> <li> Developing vision-language-action architectures that connect multimodal perception and language understanding with physical control </li> <li> Investigating reinforcement learning and imitation learning methods for sparse, delayed, or difficult-to-verify objectives </li> <li> Building scalable methods for incorporating demonstrations, teleoperation data, video, simulation trajectories, and autonomous robot experience into foundation models </li> <li> Designing capture methodologies, datasets, evaluation protocols, and data-quality pipelines for embodied learning </li> <li> Developing simulation environments and conducting sim-to-real experiments on physical robotic platforms </li> <li> Exploring planning, guided generation, and search over action trajectories </li> <li> <p data-renderer-start-pos="241