Machine Learning & Reinforcement Learning Lead

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Machine Learning & Reinforcement Learning Lead, Slough Client: Opus Recruitment Solutions
Location: Slough, United Kingdom
Job Category: Other
EU work permit required: Yes
Job Views: 2
Posted: 06.06.2025
Expiry Date: 21.07.2025
Job Description: Hot Opportunity Alert!
Central London Office
I’m thrilled to be working with one of the most exciting robotics R&D companies out there.
We’re looking for a Senior Engineer with deep expertise in reinforcement learning to help drive the development of intelligent, full-body motion capabilities. This role is ideal for someone passionate about building robust, real-world solutions for dynamic locomotion and manipulation in complex environments.
Key Responsibilities:
Design and implement learning-based control strategies for advanced locomotion tasks such as walking, balancing under load, stair climbing, and fall recovery.
Develop high-fidelity simulation environments that reflect real-world dynamics, including actuator constraints and environmental interactions.
Conduct rigorous testing in both simulated and physical environments to ensure performance and reliability.
Collaborate with multidisciplinary teams to integrate control systems into a unified robotic platform.
Required Experience & Skills:
MSc or PhD in Robotics, Control Engineering, Machine Learning, or a related field.
3+ years of experience developing control systems for legged robotic platforms.
Strong background in reinforcement learning applied to robotic control.
Deep understanding of humanoid robot dynamics and control theory.
Proven experience with deploying algorithms on physical robots, including hardware-in-the-loop testing.
Strong programming skills in Python and C++.
Familiarity with hybrid control systems that combine classical and learning-based approaches.
Bonus Skills:
Experience with real-time control systems and minimizing latency in robotic applications.
Knowledge of trajectory optimization and motion planning under uncertainty.
Exposure to collaborative or multi-agent robotic systems.
Understanding of safety-critical control strategies and system-level fault tolerance.

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