About
Reliable movement in real environments requires more than classical control or pure ML alone. This role bridges both.
This is an early-stage robotics company building the intelligence layer for industrial machines – forklifts, cranes, excavators and other heavy equipment. The approach is pragmatic: start with remote teleoperation to generate real-world operator data at scale, then train Vision-Language-Action models toward increasing autonomy.
You’ll work on locomotion and control systems that combine learned and deterministic behaviour.
What you'll do
- Develop control systems for robotic platforms
- Apply RL to improve behaviour
- Integrate learning into control loops
- Test systems on real hardware
- Improve stability and performance
- Work across simulation and deployment
What you'll need
- Strong robotics or control systems background
- Experience with reinforcement learning
- Python and ML framework experience
- Understanding of real-world constraints
- Ability to ship systems end-to-end
Shortlisted candidates will be contacted within 48 hours.
