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via Greenhouse · 17 September 2026 ·4 days ago

Senior ML Engineer (AI Research, Physical AI)

nebius

Israel; Remote - Europe; United Kingdom Remote

This listing is from Greenhouse

View original listing ↗

<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
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Reinforcement and imitation learning from human demonstrations, simulation, and real-world experience
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Scalable collection, generation, and curation of multimodal embodied data
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Simulation, world models, and sim-to-real transfer
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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
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Scalable acquisition and generation of human, robot, and simulated interaction data
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World models, planning, and model-based control
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Sim-to-real transfer, domain adaptation, and robust policy evaluation
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Dexterous manipulation, whole-body control, and general-purpose robotic agents
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</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
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<li>
Investigating reinforcement learning and imitation learning methods for sparse, delayed, or difficult-to-verify objectives
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<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
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<li>
Developing simulation environments and conducting sim-to-real experiments on physical robotic platforms
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Exploring planning, guided generation, and search over action trajectories
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<li>
Prototyping new capabilities in areas such as dexterous manipulation, mobile manipulation, and whole-body control
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Writing robust research software and distributed training infrastructure that enable rapid experimentation
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<li>
Collaborating with research and engineering teams to translate promising ideas into reliable real-world systems
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<li>
Communicating results through technical reports, open-source releases, demonstrations, and research publications
</li>
</ul>
We expect you to have:
<ul class="ak-ul" data-local-id="c48d5038-9ea2-4496-8079-80e9dd7267d0" data-indent-level="1">
<li>
A profound understanding of the theoretical foundations of machine learning, reinforcement learning, or robot learning
</li>
<li>
Deep expertise in at least one relevant area, such as reinforcement learning, imitation learning, multimodal generative modeling, computer vision, robotics, planning, or control
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Experience training and evaluating modern deep learning models, including transformer-based or multimodal foundation models
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Substantial experience training large models across multiple computational nodes
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Strong software engineering and algorithm-design skills; we primarily use Python
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Deep experience with a modern deep learning framework; we primarily use JAX
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Experience designing, executing, and analyzing machine learning experiments with appropriate statistical rigor
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Ability to formulate meaningful research questions, design experiments that test clear hypotheses, and draw defensible conclusions
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Experience implementing research ideas and iterating quickly across modeling, data, infrastructure, and evaluation
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Strong communication and leadership abilities, including the ability to collaborate across research and engineering disciplines
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Ability to document research findings clearly and contribute to technical reports or research publications
</li>
</ul>
Nice to have:
<ul class="ak-ul" data-local-id="82adb5f9-6e72-4406-a43f-33df64a89ac1" data-indent-level="1">
<li>
Experience working with real-world robots and robotic simulation environments
</li>
<li>
Experience with dexterous manipulation, whole-arm manipulation, mobile manipulation, or humanoid robotics
</li>
<li>
Experience with multimodal sensing, including tactile, force-torque, depth, and proprioceptive signals
</li>
<li>
Experience collecting human demonstrations through teleoperation, motion capture, wearable devices, or observation
</li>
<li>
Experience developing or post-training vision-language models, vision-language-action models, or video and world models
</li>
<li>
Experience with deep reinforcement learning techniques such as offline <span data-highlighted="true" data-vc="highlighted-

This listing is from Greenhouse. View original listing ↗

Senior ML Engineer (AI Research, Physical AI)

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