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Senior ML Engineer (AI Research, Physical AI)

Nebius•Technology
DeutschlandFull-timeOn sitelast month
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📄Stellenbeschreibung

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 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 def

Job overview

Posted
17 September 2026
Location
Deutschland
Employment type
Full-time
Work model
On site

Company

N

Nebius

Amsterdam, Netherlands; Berlin, Czech Republic; Remote - Europe
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Source

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