Robotics Engineer - Embodied AI

About

We are working with a well-funded deep-tech robotics team building next-generation systems that bridge vision, language, and action in the physical world. The focus is on deploying learning-based models directly onto real robotic hardware, moving beyond simulation-heavy research into production-grade embodied AI.

This is a hands-on engineering role for someone who has built and deployed learning systems that interact with the real world - not just trained models in isolation.

What you’ll be working on

• Building and deploying Vision-Language-Action (VLA) and robotics foundation models on real robotic platforms
• Developing end-to-end learning pipelines from data collection training evaluation real-world deployment
• Working directly with robotic hardware (manipulation systems / mobile robots depending on project scope)
• Designing large-scale data pipelines from multimodal robot sensor streams (vision, depth, proprioception, action logs)
• Running structured experimentation across architectures, datasets, and training strategies for physical AI systems
• Improving Sim2Real transfer and closing the gap between model performance in simulation and real-world environments
• Optimising inference and latency for real-time robotic control loops

What we’re looking for

• 3 years experience in ML/robotics/computer vision with at least some exposure to real-world robotic systems
• Hands-on experience with VLA, robotics policy learning, RL, imitation learning, or multimodal transformer models
• Experience working across the robotics stack (e.g. ROS, simulation tools such as Isaac / Gazebo / MuJoCo, sensor integration)
• Strong track record of data-driven experimentation (not purely intuition-led iteration)
• Experience building or owning data pipelines for ML systems (collection, filtering, labelling, evaluation)
• Comfortable working close to hardware and debugging real-world system behaviour
• Strong Python and deep learning framework experience (PyTorch preferred)

Nice to have

• Experience deploying models directly onto robots (arms, mobile robots, drones, or similar)
• Exposure to large-scale training (multi-GPU / distributed training)
• Experience with Sim2Real transfer or domain randomisation
• Background in transformer-based robotics policies or foundation models
• Experience with real-time systems or embedded deployment constraints

Why this role is interesting

• You’ll work directly on embodied AI systems operating in the real world, not just simulation research
• High autonomy across model design, data strategy, and deployment
• Opportunity to shape core robotics intelligence systems from the ground up
• Strong focus on shipping systems that work outside the lab

Location / setup

Flexible within Europe. Primarily remote working.