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.