Staff Controls Software Engineer
On-siteLondon, England, United Kingdom
Job Summary
Design and maintain modular, real-time control architectures supporting locomotion, manipulation, and teleoperation. Implement high-performance control loops and middleware interfaces for deterministic, safe operation. Industrialise algorithms, establish clear software boundaries, and translate research prototypes into scalable, maintainable control software. Apply modern practices including CI/CD, code reviews, and automated testing frameworks covering simulation and hardware-in-the-loop. Define reliability, latency, and safety metrics; lead integration on embedded or Linux systems; and drive release management for the control stack. Conduct profiling, benchmarking, and performance validation on robotic hardware while implementing software-based fail-safes and redundancy mechanisms. Mentor engineers on best practices and guide the long-term control software roadmap.
Required Qualifications
- B.S., M.S., or Ph.D. in Robotics, Control, Computer Engineering, or a related field
- 10+ years of experience developing robotics control software for real-time systems
- Proven expertise in C++ (modern standards) and Python for software implementation, testing, and automation
- Strong background in control theory, robot kinematics & dynamics, and real-time control systems
- Experience deploying and validating control software both in simulation and on physical robotic platforms
- Solid understanding of software architecture, version control (Git), CI/CD pipelines, and automated testing
- Excellent debugging, profiling, and system performance optimisation skills
- Track record of shipping production-quality robotics software, not simply research prototypes
- Proven experience managing the software lifecycle, including release management and cross-team integration
Desired Qualifications
- Experience with ROS2, real-time Linux, or RTOS environments
- Familiarity with relevant tools and libraries such as Eigen, Pinocchio, Placo, PyTorch and MuJoCo
- Familiarity with safety-critical systems, redundancy, and failover mechanisms
- Background in whole-body control, actuator coordination, or real-time trajectory execution
- Experience in Joint/Cartesian Impedance and force control
- Strong communication and leadership skills, with the ability to influence architecture decisions and engineering culture
- Experience in learning-based control methods such as reinforcement learning (RL) or imitation learning
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