Senior ML Engineer, AI Research – Physical AI

This is a fully remote position, open to applicants in Netherlands.

📋 Description

• Adapt large foundational models and learning algorithms tailored for robotic agents.

• Develop prototypes for new features in simulation and validate effective strategies on real-world systems.

• Design, implement, train, and assess large models and learning algorithms for robotic agents.

• Create vision-language-action architectures that integrate multimodal perception and language comprehension with physical control.

• Examine reinforcement learning and imitation learning techniques for complex objectives.

• Construct scalable methods that incorporate demonstrations, teleoperation data, video, simulation trajectories, and experiences of autonomous robots into foundational models.

• Develop capturing methodologies, datasets, evaluation protocols, and data-quality pipelines for embodied learning.

• Create simulation environments and perform sim-to-real experiments on physical robotic platforms.

• Investigate planning, guided generation, and searching over action trajectories.

• Prototype functionalities in dexterous manipulation, mobile manipulation, and whole-body control.

• Develop robust research software and infrastructure for distributed training.

• Collaborate with research and engineering teams to turn concepts into dependable real-world systems.

• Convey findings through technical reports, open-source releases, demonstrations, and research publications.

⛳️ Requirements

• In-depth understanding of the theoretical principles of machine learning, reinforcement learning, or robot learning.

• Extensive expertise in at least one relevant field, such as reinforcement learning, imitation learning, multimodal generative modeling, computer vision, robotics, planning, or control.

• Experience in training and evaluating contemporary deep learning models, including transformer-based or multimodal foundational models.

• Significant experience in training large models across several computational nodes.

• Strong software engineering and algorithm design capabilities, primarily in Python.

• Extensive experience with a modern deep learning framework, primarily JAX.

• Experience in designing, executing, and analyzing machine learning experiments with statistical rigor.

• Capability to formulate meaningful research questions, design hypothesis-testing experiments, and derive sound conclusions.

• Experience in implementing research ideas across modeling, data, infrastructure, and evaluation.

• Strong communication and leadership skills across research and engineering domains.

• Ability to document research outcomes 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 in multimodal sensing.

• Nice to have: experience collecting human demonstrations.

• Nice to have: experience in 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 discipline or equivalent practical experience.

• Nice to have: impactful publications, open-source contributions, or deployed robotic systems.

• Nice to have: experience in engineering large distributed data-processing, simulation, or model-training systems.

• Nice to have: a record of building and delivering products or research prototypes.

• Nice to have: excellent command of the English language.

• 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.

• Opportunities for career growth and learning.

• Flexibility and ownership over your work.

• A collaborative and innovative culture.

• Chance to work on impactful AI projects.

• An international environment with talented teams.

• Fast-paced work setting.

• Encouragement of bold thinking.

• Continuous growth opportunities.

• Meaningful impact on the field.

• Trust and genuine ownership in your role.

• Opportunity to influence the future of AI.