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..