Senior ML Engineer – AI Research, Physical AI

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🔥 1 minute ago

🌐 Israel, United Kingdom – Remote

🇮🇱 Israel – Remote 🇬🇧 United Kingdom – Remote

⏰ Full Time

🟠 Senior

🧠 AI Research Scientist

👻 Ghost score 25%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Nebius Group

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

🤖 Artificial Intelligence

🏢 Enterprise

☁️ SaaS

Artificial Intelligence • Enterprise • SaaS

Nebius Group is building one of the world’s leading AI infrastructure companies, focusing on providing the necessary compute, storage, and tools for developers in the AI space. Based in Europe and listed on Nasdaq, Nebius has a global presence with R&D centers across Europe, North America, and Israel. The company's primary offering is an AI-centric cloud platform designed for intensive AI workloads, complemented by various other businesses involved in generative AI development, edtech, and autonomous technology.

📋 Description

• Conduct applied research in Physical AI for intelligent agents that perceive, reason, and act in the physical world • Modify large foundation models and learning algorithms for robotic agents • Prototype new capabilities in simulation and validate approaches on real-world systems • Design, implement, train, and evaluate large models and learning algorithms • Develop vision-language-action architectures connecting multimodal perception and language understanding with physical control • Investigate reinforcement and imitation learning methods for difficult objectives • Build scalable methods incorporating demonstrations, teleoperation data, video, simulation trajectories, and autonomous robot experience into foundation models • Design capture methodologies, datasets, evaluation protocols, and data-quality pipelines for embodied learning • Develop simulation environments and conduct sim-to-real experiments on physical robotic platforms • Explore planning, guided generation, and search over action trajectories • Prototype dexterous manipulation, mobile manipulation, and whole-body control capabilities • Write robust research software and distributed training infrastructure • Collaborate with research, infrastructure, and engineering teams to translate ideas into reliable real-world systems • Communicate results through technical reports, open-source releases, demonstrations, and research publications

🎯 Requirements

• 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; primarily using Python • Deep experience with 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 defensible conclusions • Experience implementing research ideas and iterating quickly across modeling, data, infrastructure, and evaluation • Strong communication and leadership abilities, including collaboration across research and engineering disciplines • Ability to document research findings clearly and contribute to technical reports or research publications • Applicants must be authorized to work in the country in which they apply and provide proof of employment eligibility as a condition of hire • Nice-to-have: experience with real-world robots and robotic simulation environments • Nice-to-have: experience with dexterous, whole-arm, mobile, or humanoid robotics • Nice-to-have: experience with multimodal sensing • Nice-to-have: experience collecting human demonstrations • Nice-to-have: experience developing or post-training vision-language, vision-language-action, video, or world models • Nice-to-have: experience with deep reinforcement learning techniques such as offline RL, actor-critic methods, PPO, reward modeling, preference learning, or model-based RL • Nice-to-have: familiarity with MuJoCo, Isaac Sim, Isaac Lab, PyBullet, ROS, or equivalent systems • Nice-to-have: knowledge of FSDP, ZeRO, FlashAttention, mixed-precision training, quantization, and distributed checkpointing • Nice-to-have: PhD in Computer Science, Robotics, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience • Nice-to-have: track record of impactful publications, open-source contributions, or deployed robotic systems • Nice-to-have: experience engineering large distributed data-processing, simulation, or model-training systems • Nice-to-have: record of building and delivering products or research prototypes • Nice-to-have: excellent command of English and strong technical writing, presentation, and communication skills • Nice-to-have: proficiency in version control, testing, code review, and CI/CD

🏖️ Benefits

• Competitive compensation • Career growth and learning opportunities • Flexibility and ownership • Collaborative and innovative culture • Opportunity to work on impactful AI projects • International environment and talented teams • Fast moving • Bold thinking • Constant growth • Meaningful impact • Trust and real ownership • Opportunity to shape the future of AI