Robot Autonomy Scientist — RL & Dexterous Manipulation

What you'll do

• Design and develop robot autonomy software stack and algorithms to enable capabilities including grasping and more dexterous behaviors in unstructured environments
• Research and implement state-of-the-art robot learning policies, including reinforcement learning and imitation learning-based techniques
• Build reliable, high-speed robot autonomy software stack optimized for inference performance
• Design and maintain robust data collection and curation pipelines for production robot fleets
• Optimize robot policies for distributed training at scale and real-time edge deployment
• Ship production quality, safety-critical software
• Advance SOTA dexterous manipulation research through novel methodologies while bridging theory & practice—real customer use-cases with clear success criteria.

• PhD or MS degree in Computer Science, Machine Learning, Robotics, or equivalent technical discipline
• Deep expertise in machine learning fundamentals, reinforcement learning, and associated frameworks (PyTorch, TensorFlow, Ray, etc.)
• 3+ years of proven track record developing and deploying ML systems from research through production implementation
• Hands-on experience with model lifecycle management including training, deployment, and maintenance in production settings

• Authored or co-authored peer-reviewed publications in robotics or related fields
• Hands-on experience designing and implementing bimanual manipulation tech stacks with imitation learning or RL-based methods
• Background in real-time ML inference systems, simulation-to-reality transfer, or advanced reinforcement learning implementations

• We support publishing at top robotics/ML venues and presenting at conferences (travel + time fully covered).
• Medical, dental & vision plans
• Daily meals stipend

• $150,000 - $250,000 annual salary + cash and stock awards + benefits
• The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.