Senior Simulation Engineer - Robotics Manipulation Simulation

Secure Consult; Greater London; Hybrid; GBP 65,000 - 90,000; Full time

Job summary

Secure Consult in London is seeking an experienced engineer to design and maintain physics-based robotics simulation environments for manipulation tasks. You will work on reinforcement learning policy training and optimizing sim-to-real transfer.

Collaboration with vendors for high-quality 3D assets and scalable learning infrastructure is essential. Required 3+ years in simulation or game-engine projects, strong Python skills, and experience with Isaac Sim, MuJoCo, or PyBullet.

Qualifications

• 3+ years in simulation environment development or game-engine-based interactive systems
• Proficient with physics simulators and Python programming
• Experience with RL training, domain knowledge in robotics is a plus

Responsibilities

• Design and implement simulation gym environments for manipulation tasks across domains
• Define observation/action spaces and reward functions for RL
• Tune physics parameters and asset fidelity to minimize sim-to-real gaps
• Collaborate with vendors to validate 3D assets for simulation
• Analyze and mitigate reward hacking for robust learned behaviors
• Identify performance bottlenecks to enable faster training
• Enhance RL infrastructure for scalable evaluation and training

Skills

Simulation development

Python

Reinforcement learning

Software engineering practices

Documentation

Tools

Isaac Sim

MuJoCo

PyBullet

Unreal

Unity

GPU acceleration

Job description

London - Fully onsite Full time SC-EB3D918C385F37534F5E

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
• 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.

simulation robotics reinforcement learning physics simulation Python GPU acceleration RL policy training manipulation