Senior ML Engineer (AI Research, Physical AI)

About This Opportunity

We are representing a confidential AI cloud infrastructure company at the forefront of physical AI research. Our client is building intelligent agents capable of perceiving, reasoning, and acting in the physical world through advanced vision-language-action models, reinforcement learning, and robotic control systems.

We're seeking a Senior ML Engineer to join their applied AI research team in Europe (Netherlands or UK). This is an opportunity to work on cutting-edge robotics research, from modifying foundation models for robotic agents to validating approaches on real-world systems.

Responsibilities

Your work will span the full research lifecycle in physical AI:

• 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 complex, real-world objectives
• Build scalable methods for 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 new capabilities in dexterous manipulation, mobile manipulation, and whole-body control
• Write robust research software and distributed training infrastructure enabling rapid experimentation
• Collaborate with research and engineering teams to translate promising ideas into reliable real-world systems
• Communicate results through technical reports, open-source releases, demonstrations, and research publications

What We're Looking For

Required qualifications:

• Profound understanding of theoretical foundations in machine learning, reinforcement learning, or robot learning
• Deep expertise in at least one area: 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; Python proficiency required
• Deep experience with modern deep learning frameworks; JAX is primarily used
• Experience designing, executing, and analyzing ML experiments with appropriate statistical rigor
• Ability to formulate meaningful research questions, design hypothesis-driven experiments, and draw defensible conclusions
• Experience implementing research ideas and iterating quickly across modeling, data, infrastructure, and evaluation
• Strong communication and leadership abilities across research and engineering disciplines
• Ability to document research findings clearly and contribute to technical reports or publications

Preferred qualifications:

• Experience with real-world robots and robotic simulation environments
• Experience with dexterous manipulation, whole-arm manipulation, mobile manipulation, or humanoid robotics
• Experience with multimodal sensing (tactile, force-torque, depth, proprioceptive signals)
• Experience collecting human demonstrations through teleoperation, motion capture, wearable devices, or observation
• Experience developing or post-training vision-language models, vision-language-action models, or world models
• Experience with deep RL techniques: offline RL, actor-critic methods, PPO, reward modeling, preference learning, or model-based RL
• Familiarity with robotics tools and simulators such as MuJoCo, Isaac Sim, Isaac Lab, PyBullet, ROS, or equivalent
• Knowledge of scalable training techniques: FSDP, ZeRO, FlashAttention, mixed-precision training, quantization, distributed checkpointing
• PhD in Computer Science, Robotics, Machine Learning, AI, or related field, or equivalent practical experience
• Track record of impactful publications, open-source contributions, or deployed robotic systems
• Experience engineering large distributed data-processing, simulation, or model-training systems

How We Work With You

When you apply through Get A Job.ai, our talent team will review your background and conduct an initial screening to understand your experience and interest. If there's a strong match, we'll submit your profile to our client for consideration. Please note that candidates should apply only through Get A Job.ai and should not contact the client directly during this process.

Our client offers competitive compensation, meaningful opportunities for career growth, and the chance to shape the future of physical AI in a fast-moving, innovative environment. You'll work alongside talented engineers and researchers in a collaborative international setting with real ownership over impactful projects.

Pay

Compensation details will be discussed during the screening process based on experience level and location.

Equal Opportunity: Get A Job.ai is committed to inclusive hiring practices. We welcome applications from candidates of all backgrounds and provide equal employment opportunities without regard to race, color, religion, sex, national origin, age, disability, or any other protected characteristic.