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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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