Senior ML Engineer, AI Research – Physical AI

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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

• Modify large foundation models and learning algorithms for robotic agents • Prototype new capabilities in simulation and validate promising approaches on real-world systems • Design, implement, train, and evaluate large models and learning algorithms for robotic agents • Develop vision-language-action architectures connecting multimodal perception and language understanding with physical control • Investigate reinforcement learning 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 capabilities in dexterous manipulation, mobile manipulation, and whole-body control • Write robust research software and distributed training infrastructure • Collaborate with research and engineering teams to translate ideas into reliable real-world systems • Communicate results through technical reports, open-source releases, demonstrations, and research publications

🎯 Requirements

• Profound understanding of 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 a modern deep learning framework; primarily using JAX • Experience designing, executing, and analyzing machine learning experiments with statistical rigor • Ability to formulate meaningful research questions, design hypothesis-testing experiments, and draw defensible conclusions • Experience implementing research ideas across modeling, data, infrastructure, and evaluation • Strong communication and leadership abilities across research and engineering disciplines • Ability to document research findings and contribute to technical reports or research publications • 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 including 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: a PhD in a relevant technical field or equivalent practical experience • Nice to have: 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 • Nice to have: proficiency in version control, testing, code review, and CI/CD • Applicants must be authorized to work in the country in which they apply and provide proof of employment eligibility

🏖️ 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