Robotics AI Engineer
Location: On-site — San Francisco Bay Area
About Our Client
Our client is an applied robotics R&D company building a next-generation humanoid robot platform and the full software stack that powers it. They work across the full stack: hardware architecture, locomotion, autonomy, simulation, and infrastructure. The team moves fast, ships to real robots, and believes the best ideas should be built and tested in the physical world, not just in a lab.
The Role
As a Robotics AI Engineer, you'll work at the intersection of learning and hardware, training and deploying policies that run on our client's humanoid platform in the real world. This is not a research role in the traditional sense. You'll be expected to get results on physical robots, not just in simulation, and to iterate quickly when things break. You'll work closely with the hardware, firmware, and infrastructure teams to close the loop between training and deployment.
What You'll Do
- Design and train RL and imitation learning policies for locomotion, manipulation, or whole-body control
- Run experiments on physical hardware and close the sim-to-real gap through systematic debugging and domain adaptation
- Build and maintain simulation environments and data pipelines that support fast policy iteration
- Instrument robot deployments and analyze failure modes to feed improvements back into training
- Collaborate directly with hardware and firmware engineers to understand physical constraints and improve policy robustness
What Our Client Is Looking For
- Strong foundations in reinforcement learning or imitation learning, with hands-on experience training policies that run on real systems
- Comfort working directly with robots, not just simulators
- Proficiency in Python and familiarity with standard RL/ML frameworks
- An empirical, debugging-first mindset
- Ability to move fast and context-switch between research problems and engineering tasks
Nice to Have
- Prior work on humanoid or legged robot platforms
- Experience with sim-to-real transfer techniques (domain randomization, system identification, noise injection)
- Contributions to open-source robotics projects
- Background in control theory, trajectory optimization, or dynamics