Reinforcement Learning (RL) Engineer, Manipulation

Overview

Develop and train RL-based manipulation policies for humanoid robots in simulation and real world.

Key Responsibilities

• rl pipelines
• behavior cloning
• team collaboration
• policy training
• task suite
• sim2real

Tasks

-Partner with testing and operations to establish real-world RL training pipelines. -Partner with teleoperations to collect trajectories in simulation for behavior cloning. -Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics -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. -Experiment with various ways of bringing policies trained in simulation to the real world..

Requirements

• rl
• robotics
• llms
• pytorch
• simulators
• publications

What You Bring

-Experience building infrastructure for large-scale RL (e.g. using ray). -Experience in RL for robotics. -Hands‑on with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference. -3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it. -Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open VLA frameworks. -You are self-driven, pro-active, communicate efficiently, document experiments clearly and communicate trade‑offs crisply. -Publications at ICLR/ICML/NeurIPS or equivalent open‑source contributions. -Experience with simulators for robotics (Isaac Sim, MuJoCo etc.) -Freedom to influence the product and own key initiatives -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.

People Also Searched For

Benefits

-Paid vacation with adjustments based on your location to comply with local labor laws, and additional paid sick leave days -Competitive salary plus participation in our Stock Option Plan -Office perks: free breakfasts, lunches, snacks, and regular team events -Travel opportunities to our Vancouver and Boston offices

About Humanoid

-Blends advanced AI, multimodal vision reasoning, and modular hardware into a robust platform targeting logistics, manufacturing, and retail. -The company pivots on commercial impact—solving repetitive physical tasks in real settings, not just lab experiments. -A cinematic teaser video set the tone for its vision: human-robot coexistence in everyday environments. -A standout is its modular design allowing interchangeable platforms—wheeled or legged—aimed at rapid and affordable deployment.

Sector Specialisms