Reinforcement Learning (Rl) Engineer

We're looking for a Reinforcement Learning (RL) Engineer to develop and deploy learning-based control policies for our robots, including integration with Vision-Language-Action (VLA) stacks. You will own the training loop from simulation and logged data through evaluation on hardware, working closely with simulation, perception, and robotics teams. This is not a research-only role. You will ship policies that must work under real operational constraints—latency, safety, embodiment differences, and continuous improvement from field data. What you'll do Design, implement, and maintain RL training pipelines for robotic manipulation, navigation, and whole-body control tasks Develop and tune policies in simulation and on real hardware, with clear benchmarks for success, robustness, and regression detection Integrate RL stacks with VLA and broader autonomy systems: action spaces, planners, low-level controllers, and deployment interfaces Build reward design, curriculum learning, and domain randomization strategies that improve sim-to-real transfer Own dataset and experience pipelines (sim rollouts, teleoperation logs, filtered trajectories) for offline RL, imitation, and hybrid training Implement evaluation harnesses in sim and on physical robots; analyze failure modes and drive iterative improvements Collaborate with simulation engineers on environments, assets, and synthetic data needed for scalable training Work with software and embedded teams on inference deployment, monitoring, and safe rollout of new policy versions Document experiments, model checkpoints, and deployment procedures so the team can reproduce and extend your work Set the technical bar for the RL team: review other engineers' training pipelines and policies, mentor junior and mid-level engineers, and raise the group's standard on reproducibility and evaluation rigor. Define the framework for deciding when a policy is safe to deploy, setting the evaluation gates, safety thresholds, and regression checks a model must clear before it reaches hardware, and make the deployment recommendation for each policy version.