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.

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.

• 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

• 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