Reinforcement Learning (RL) Engineer, Manipulation

What You’ll Do:

• Train language-vision conditioned manipulation policies via reinforcement learning (RL) in simulation and in the real world.
• Construct challenging and diverse suites of manipulation tasks in simulation.
• Partner with teleoperations to collect trajectories in simulation for behavior cloning.
• Partner with testing and operations to establish real-world RL training pipelines.
• Experiment with various ways of bringing policies trained in simulation to the real world..

We’re Looking For:

• 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it.
• Hands‑on with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference.
• Experience solving real problems using reinforcement learning with deep neural networks in any domain.
• Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code.
• You are self-driven, pro-active, communicate efficiently, document experiments clearly and communicate trade‑offs crisply.

Nice to have

• Experience with simulators for robotics (Isaac Sim, MuJoCo etc.)
• Experience in RL for robotics.
• Experience building infrastructure for large-scale RL (e.g. using ray).
• Publications at ICLR/ICML/NeurIPS or equivalent open‑source contributions.
• Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open VLA frameworks.