Reinforcement Learning Engineer

Responsibilities

• 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

Additional Information

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.

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Company Intel

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