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
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
Required Qualifications
Master’s/PhD 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
Preferred Qualifications
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
Benefits
We support publishing at top robotics/ML venues and presenting at conferences (travel + time fully covered).
Advance SOTA dexterous manipulation research through novel methodologies while bridging theory & practice—real customer use‑cases with clear success criteria.