Reinforcement Learning Engineer - Manipulation

Secure Consult; Greater London; Hybrid; GBP 70.000 - 110.000; Vollzeit

Zusammenfassung

Secure Consult in London is seeking a hands-on ML engineer to advance reinforcement learning for robotic manipulation. You will work across simulation and real-world environments, building robust policies and deployment pipelines.

The role requires 3+ years in deep learning for robotics, strong Python and PyTorch or JAX skills, and familiarity with robotics simulators like MuJoCo or Isaac Sim. Onsite, full-time position in London.

Qualifikationen

• 3+ years building deep learning systems with delivered models or publications.
• Experience applying reinforcement learning with deep neural networks in practical domains.
• Strong proficiency in Python and PyTorch or JAX.

Aufgaben

• Develop and train reinforcement learning models for robotic manipulation tasks in simulation and real-world settings.
• Collaborate across teams to create robust manipulation policies and integration pipelines.
• Design diverse manipulation tasks in simulation and contribute to deployment workflows.
• Collect trajectories with teleoperation teams for behavior cloning.
• Establish real-world RL training pipelines and bridge sim-to-real gaps.
• Document trade-offs and present clear results to stakeholders.

Kenntnisse

Deep learning

Reinforcement learning

Python

PyTorch

JAX

Robotics simulation

Tools

MuJoCo

Isaac Sim

Ray

Jobbeschreibung

London - Fully onsite Full time

The opportunity

Join a forward-thinking autonomy team developing advanced humanoid robots. In this role, you will develop and train reinforcement learning models for robotic manipulation tasks, working both in simulation and real-world environments. Collaborate across teams to create robust manipulation policies, contribute to deployment pipelines, and innovate solutions to bridge simulation and reality performance gaps.

What you could work on

• Train language-vision conditioned manipulation policies using reinforcement learning in simulation and real-world settings
• Design and implement diverse and challenging manipulation tasks in simulation
• Collaborate with teleoperation teams to collect trajectories for behavior cloning
• Work with testing and operations to establish real-world RL training pipelines
• Experiment with methods to transfer policies from simulation to physical robots
• 3+ years building deep learning systems with delivered models or publications
• Hands-on experience with at least one of: large language models, vision-language models, or image/video generative models
• Experience applying reinforcement learning with deep neural networks in practical domains
• Strong proficiency in Python and PyTorch or JAX, including debugging and profiling numerical code
• Self-driven with clear communication, documentation, and concise presentation of trade-offs
• Desirable: experience with robotics simulators (e.g., Isaac Sim, MuJoCo)
• Desirable: experience in RL for robotics and large-scale RL infrastructure (e.g., ray)
• Desirable: publications at major ML conferences or contributions to open source

Security vetting

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Working arrangements

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

reinforcement learning robotics deep learning simulation manipulation Python PyTorch machine learning