Research Scientist - Reinforcement Learning, Robotics
Conducts research in reinforcement learning and VLA post-training for robotics and autonomous systems, focusing on dexterous manipulation. Publishes at top conferences and deploys algorithms to real-world products. Requires MSc/PhD, strong publications, and expertise in Python, PyTorch, CV, robotics.
About the job
Responsibilities
- Conduct research on reinforcement learning (RL) related topics including large-scale closed-loop RL and VLA post-training with applications to robotics with emphasis on dexterous manipulation
- Dive into fundamental and relevant topics on RL with broader applications
- Work closely with other Research Scientists and interns on research publications for submission to top-tier conferences
- Collaborate with Research Engineers and engineering teams to test and deploy algorithms to our autonomy and robotics products
Requirements
- Strong research record in the fields of RL and VLA post-training for robotics and autonomous systems, with publications in top-tier conferences or journals in the fields of computer vision, machine learning, and robotics
- MSc or PhD in machine learning and computer vision with autonomy and robotics applications or closely-related fields
- Passion for next-generation, scalable autonomy and robotics for real-world systems
- Strong research skills and the ability to work both independently and collaboratively on projects
- Technical experience in: Python, PyTorch, computer vision, robotics systems, and distributed machine learning model training
Nice to Have
- Hands-on experience in at least one of the following fields:
- VLA post-training for autonomy or robotics
- Large-scale closed-loop RL in robotic simulation
- Large-scale RL training infrastructure
Compensation
- Base salary range: $126,000 - $423,000 USD annually
- Equity, comprehensive health/dental/vision/life/disability insurance, 401k with employer match, learning/wellness stipends, paid time off
Skills
Reinforcement Learning, PyTorch, Python, Computer Vision, Robotics, Distributed Machine Learning, Vla Post-Training, Closed-Loop Rl
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