Research Member of Technical Staff- Post-training & Robot Learning

Highlights

• Adapt web-pretrained video models to real robot tasks with minimal data
• Build RL and post-training pipelines that turn predictions into robot actions
• Iterate fast between simulation and real hardware to close performance loop

Ideal candidate

Hands-on roboticist who lives for real hardware and fast iteration. You’re comfortable with ambiguity, obsessed with reliable real-world behavior, and eager to own research on a small team.

Requirements

• Hands-on experience with robot systems, robotic policy learning, or autonomous systems
• Strong understanding of imitation learning, behavior cloning, and how RL builds on it
• Practical familiarity with real robot hardware, deployment constraints, and sensor modalities
• Solid ML skills with hands-on PyTorch experience
• Ability to diagnose policy failures, reason about distribution shift, and iterate on data and training
• Comfort with ambiguity and fast-changing research priorities