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.