Machine Learning & Reinforcement Learning Lead

Opus Recruitment Solutions
London
2 days ago
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🚨Hot Opportunity Alert!🚨

đź’ĄSalary: ÂŁ120,000 - ÂŁ150,000

📍Central London Office📍


I’mthrilledto be working with one of themost exciting robotics R&D companiesout 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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