Machine Learning Engineer

We are looking for an exceptional Senior or Staff RL Control Engineer to join our Control Team in London.

You will be a key contributor to the development and evolution of our whole-body control (WBC) software stack - the layer that unifies locomotion, manipulation, and interaction control for our robotic systems.

The ideal candidate combines a strong background in classical control with the ability to develop and integrate reinforcement-learning-based control components into complex, real-time systems. You will work at the intersection of robot dynamics, control architecture, and modern learning-driven control, collaborating closely with engineers in London and Vancouver who share responsibility for our global control infrastructure.

A key focus of this role will be ensuring safety and robustness in loco-manipulation behaviors of bipedal robots - designing control strategies that guarantee safe, stable, predictable, and recoverable interaction between locomotion and manipulation subsystems in dynamic environments.

This is a hands-on, system-defining role for someone passionate about high-performance robotic control - from model-based design to the deployment of advanced control strategies that bring robots to life.

What You'll Do:

Whole-Body Control Architecture:

• Design, implement, and extend whole-body control frameworks that coordinate multiple robot subsystems (locomotion, manipulation, teleoperation).
• Develop and maintain mid-level controllers that translate motion objectives into coherent, stable, real-time control actions.
• Ensure controllers are modular, deterministic, and extensible, supporting both classical and learning-based control strategies.
• Architect and tune low-level controllers for balanced performance, supporting compliant behaviors for learning tasks and precise fallback modes for safety.
• Develop and enforce safety mechanisms within WBC to manage contact, stability, and recovery during combined locomotion and manipulation (loco-manipulation) behaviors.

Reinforcement Learning Integration:

• Develop and integrate RL-based controllers and policies within the WBC architecture.
• Define clear, robust interfaces between classical controllers and learned components, enabling smooth blending and fallback behaviors.
• Collaborate with the Imitation Learning and Deployment teams to ensure compatibility of runtime systems and deployment pipelines - while maintaining full ownership of control and WBC components.
• Shape RL action spaces to promote safe exploration, avoiding extreme behaviors while enabling smooth policy execution.
• Work with deployment teams to align RL outputs with hardware realities, using simulation penalties and transfer techniques for reliable rollout.

System Integration & Cross-Site Collaboration:

• Collaborate daily with control engineers across Boston, London, and Vancouver, aligning control strategies, architecture, and codebase.
• Benchmark actuator properties (like torque limits and delays) to refine simulation models, closing the sim2real gap.
• Validate controllers in simulation and hardware environments, iterating closely with system-level testing teams.
• Participate in design reviews, profiling, and performance analysis for high-impact control modules., Location: Whitehaven, Cumbria, North West, UKWe have an excellent opportunities for a SENIOR CONTROL SYSTEMS ENGINEER based in Whitehaven, Cumbria, working on a hybrid basis. The successful candidates will work in a team to design, build, test and implement industrial..., This job is brought to you by Jobs/Redefined, the UK's leading over-50s age inclusive jobs board. Job Description This role is responsible for developing and maintaining asset plans and standards for rail signalling and control systems, ensuring alignment with overall..., Job Title: AI Researcher Location: Cambridge or London, UK This is a permanent position with candidates required to do hybrid working in either Cambridge or London. Our client are looking for AI Researchers specialising in Reinforcement Learning with Human Feedback (RLHF)..., Machine Learning Research Engineer (Foundational Research) Join a cutting-edge research team at Thomson Reuters to help deliver the transformation promises of modern AI. You will design, build, and experiment with large language models (LLMs) in an academic environment...

• M.S. or Ph.D. in Robotics, Control, Mechanical Engineering, Computer Science, or related field.
• 5+ years of experience developing control software for complex robotic systems (humanoids, legged platforms, or articulated manipulators).
• Strong theoretical and practical background in classical control (model-based control, observers, optimal control, QP-based control).
• Proven ability to design and implement real-time control algorithms in C++ or Python.
• Deep understanding of robot dynamics, kinematics, and control optimization.
• Experience validating control architectures both in simulation and on physical hardware.

Nice to have:

• Experience developing or integrating reinforcement-learning-based control policies for high-DOF systems.
• Familiarity with whole-body control frameworks, including task hierarchies, optimization-based control, and constraint handling.
• Background in real-time or distributed control systems, including ROS2 or real-time middleware.
• Strong software engineering skills: modular design, benchmarking, testing, and performance profiling.
• Demonstrated ability to collaborate across geographically distributed teams and disciplines.

• Competitive salary plus participation in our Stock Option Plan
• Paid vacation with adjustments based on your location to comply with local labor laws
• Travel opportunities to our London and Vancouver offices
• Comprehensive health insurance coverage
• Freedom to influence the product and own key initiatives
• Collaboration with top-tier engineers, researchers, and product experts in AI and robotics
• Startup culture prioritizing speed, transparency, and minimal bureaucracy

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