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.