Member of Technical Staff - Robot Control

About the Company

At Machenta, we’re building intelligence that lets any robot learn any new skill as quickly as a human can: instantly, from a single example. We believe that achieving human-level adaptability in the physical world is a key milestone in the future of physical AI, and we’re already seeing glimpses of that future today. As a team, we’re united by this shared vision, and we work closely together in person to bring it to life. On the back of our $40m seed round, we’re now moving into a new office and lab space in the heart of King’s Cross, London. But we’re still right at the beginning here, and we’re looking for the right people to join us all for what lies ahead next. Robotics and machine learning span a diverse technical stack, and wherever you sit within it, everyone at Machenta takes ownership of their part of the company. If that kind of ownership resonates with you, and you’re excited about building together with an ambitious, closely-knit team, then we’d love to speak with you.

About the Role

As a Member of Technical Staff working on control, you’ll work on the foundation that lets our robots act precisely, safely and reliably in the real world. You’ll work across the control stack, from low-level torque, impedance and force control to real-time optimal control, and help shape how learned policies and model-based control work together to get the best of both. It’s a highly hands-on, collaborative role: you’ll take ideas from formulation to deployment on real hardware, working closely with everyone at Machenta. If you enjoy the moment when a robot that struggled yesterday moves smoothly today, you’ll find this work very rewarding.

In This Role You Will:

• Design, implement and tune low-level robot controllers, including torque, impedance and force control.

• Formulate and solve real-time optimal control problems, such as MPC and trajectory optimisation, under tight latency and compute constraints.

• Combine model-based controllers with learned policies to achieve robust, dexterous behaviour on real hardware.

• Bring up and validate controllers on physical robots, using rigorous testing.

• Help define how the control stack interfaces with our learning systems, shaping how policies and controllers work together across the robot.

What We Hope You’ll Bring:

• Hands-on experience designing, testing and deploying controllers on real robots, not just in simulation.

• A strong foundation in rigid-body dynamics, control theory and numerical optimisation.

• Experience with optimal control methods such as MPC or trajectory optimisation, and the practical know-how to make them run in real time.

• Strong Python and C++ skills, and experience writing performance-critical code for real-time systems.

• The ability to solve hard problems and debug complex systems spanning software and hardware.

• Clear communication of your ideas and findings to the team.
  

Bonus Points If You Have:

• Experience at an early-stage robotics or AI company.

• PhD in a relevant field.

• Experience across different robot manipulation platforms.

• Great intuition for tuning controllers and integrating them with learnt policies.

• Experience designing floating-base or whole-body controllers, for example on humanoid, legged or mobile manipulation platforms.

• Experience training and deploying RL-based controllers on real hardware, including tackling sim-to-real transfer.

Join Us

We’re setting out to change how robots learn, and we know it’s an ambitious goal. If you’re excited by the idea of robots picking up new skills the moment they’re shown, and want to help build that future from the ground up, we’d love to hear from you. This role is based in person at our office in London.