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Engineering - RoboticsLIVE ROLE

Senior Simulation Engineer - Robotics Manipulation Simulation

London - Fully onsiteFull timeSC-EB3D918C385F37534F5E
Current opportunities.

The opportunity

Join a forward-thinking engineering team to develop and maintain simulation environments for dexterous manipulation tasks in industrial, service, and home settings. This role focuses on creating realistic physics-based simulations and reinforcement learning policy training to bridge the sim-to-real gap. Collaborate with vendors to integrate 3D assets, optimize simulation performance, and enhance learning infrastructure to support rapid development cycles in robotics simulation.

What you could work on

  • Design and implement simulation gym environments for manipulation tasks across multiple domains
  • Define observation spaces, action spaces, and reward functions for reinforcement learning
  • Analyze and mitigate reward hacking to ensure robust learned behaviors
  • Tune physics parameters and improve asset fidelity to reduce sim-to-real transfer gaps
  • Collaborate with vendors to validate and integrate high-quality 3D simulation assets
  • Ensure accurate physics setup including dynamics, friction, mass properties, and joint limits
  • Identify and resolve simulation performance bottlenecks for efficient training
  • Enhance simulation evaluation and reinforcement learning infrastructure for scalability

Relevant experience

  • 3+ years in simulation environment development or game-engine-based interactive systems
  • Proficient with physics simulators such as Isaac Sim, MuJoCo, PyBullet, or equivalent game engines like Unreal or Unity
  • Experience with large-scale GPU-accelerated simulation and parallel processing
  • Strong Python programming skills with ability to profile and debug physics issues
  • Familiarity with modern software engineering practices and clear experiment documentation
  • Desirable: experience with robotics manipulation, reward function design, or reinforcement learning training
  • Knowledge of sim-to-real transfer techniques such as domain randomization or system identification
  • Publications or open-source contributions in robotics or reinforcement learning domains

Security vetting

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Working arrangements

London - Fully onsite, Full time. Confirm employment terms and any clearance requirements with your consultant.

simulationroboticsreinforcement learningphysics simulationPythonGPU accelerationRL policy trainingmanipulation