Research Scientist

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. Required Qualifications 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 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). Medical, dental & vision plans Daily meals stipend Hiring Process Phone screen + 3 virtual technical interviews + onsite Expected Compensation $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. #J-18808-Ljbffr