Humanoid
Overview
Humanoid is the first AI and robotics company in the UK, creating the world’s most advanced, reliable, commercially scalable, and safe humanoid robots. Our first humanoid robot HMND 01 is a next-gen labour automation unit, providing highly efficient services across various use cases, starting with industrial applications. We are seeking a highly skilled and motivated
Low / Mid-Level Controls Engineer
to join our Control team. Responsibilities
Design and implement low-level control algorithms for actuators. Develop advanced control schemes such as torque control, impedance control, and admittance control. Test and characterize actuators and create models for actuators under different conditions (e.g., load, speed). Analyze dynamic parameters such as friction, backdrivability, and hysteresis. Calculate and select the appropriate actuators (torque and speed) based on performance criteria for robotic applications, including manipulation and bipedal locomotion. Create and apply kinematic and dynamic models to predict actuator behavior in complex robotic systems, ensuring accurate motion planning and control. Develop inverse kinematics and inverse dynamics models for robotic systems. Address gravity and inertia compensation for complex, multi-link robotic systems. Utilize physics engines like MuJoCo or Drake to simulate the entire robotic body using URDF or other formats, including multiple actuators and environmental interactions, to test and refine system performance. Qualifications
Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Robotics, or a related field. Proficiency in programming languages such as C, C++, and Python. Solid understanding of embedded systems, real-time operating systems (RTOS), and hardware-software interfaces. Experience with communication protocols (EtherCAT, CAN, etc.) and sensor integration. Familiarity with debugging tools like oscilloscopes, logic analyzers, and in-circuit debuggers. Knowledge of safety-critical systems and standards is a strong plus. Excellent problem-solving skills, attention to detail, and ability to work in a fast-paced environment. Nice to have
Experience with advanced control algorithms such as MPC (model predictive control). Familiarity with machine learning applications in robotics and control. Prior experience with bipedal locomotion systems or multi-arm manipulation in robotics. Knowledge of modern robotic hardware (actuators, sensors, controllers, real time systems) and integration practices. 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 Boston offices Freedom to influence the product and own key initiatives Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics Startup culture prioritising speed, transparency, and minimal bureaucracy. Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology
#J-18808-Ljbffr
Humanoid is the first AI and robotics company in the UK, creating the world’s most advanced, reliable, commercially scalable, and safe humanoid robots. Our first humanoid robot HMND 01 is a next-gen labour automation unit, providing highly efficient services across various use cases, starting with industrial applications. We are seeking a highly skilled and motivated
Low / Mid-Level Controls Engineer
to join our Control team. Responsibilities
Design and implement low-level control algorithms for actuators. Develop advanced control schemes such as torque control, impedance control, and admittance control. Test and characterize actuators and create models for actuators under different conditions (e.g., load, speed). Analyze dynamic parameters such as friction, backdrivability, and hysteresis. Calculate and select the appropriate actuators (torque and speed) based on performance criteria for robotic applications, including manipulation and bipedal locomotion. Create and apply kinematic and dynamic models to predict actuator behavior in complex robotic systems, ensuring accurate motion planning and control. Develop inverse kinematics and inverse dynamics models for robotic systems. Address gravity and inertia compensation for complex, multi-link robotic systems. Utilize physics engines like MuJoCo or Drake to simulate the entire robotic body using URDF or other formats, including multiple actuators and environmental interactions, to test and refine system performance. Qualifications
Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Robotics, or a related field. Proficiency in programming languages such as C, C++, and Python. Solid understanding of embedded systems, real-time operating systems (RTOS), and hardware-software interfaces. Experience with communication protocols (EtherCAT, CAN, etc.) and sensor integration. Familiarity with debugging tools like oscilloscopes, logic analyzers, and in-circuit debuggers. Knowledge of safety-critical systems and standards is a strong plus. Excellent problem-solving skills, attention to detail, and ability to work in a fast-paced environment. Nice to have
Experience with advanced control algorithms such as MPC (model predictive control). Familiarity with machine learning applications in robotics and control. Prior experience with bipedal locomotion systems or multi-arm manipulation in robotics. Knowledge of modern robotic hardware (actuators, sensors, controllers, real time systems) and integration practices. 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 Boston offices Freedom to influence the product and own key initiatives Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics Startup culture prioritising speed, transparency, and minimal bureaucracy. Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology
#J-18808-Ljbffr