Research Engineer/Scientist, Reinforcement Learning Research · San Francisco · Full-time →

Tau Robotics, Inc.; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Tau Robotics, Inc. is building a general-purpose AI for robots and is seeking a researcher to develop model-based reinforcement learning algorithms.

You will collaborate with researchers focused on imitation learning and world models, contributing to end-to-end robotic autonomy. Applicants should have a strong background in deep learning and RL/IL, proven debugging of RL methods, and proficiency in Python with JAX or PyTorch, plus a track record of publications or equivalent evidence in top

Qualifications

• Strong background in deep learning and reinforcement or imitation learning.
• Proven ability to implement and debug reinforcement learning algorithms.
• Publications at premier venues (NeurIPS, ICLR, ICML, CVPR, RSS, CoRL, ICRA), or equivalent public evidence.

Responsibilities

• Create and deploy model-based reinforcement learning algorithms.
• Partner with researchers specializing in imitation learning and world models.

Skills

Deep learning

Reinforcement learning

Imitation learning

Python

Tools

JAX

PyTorch

Job description

Our mission is to build a general-purpose AI for robots. We believe the path is to deploy robots early in real customer environments, learn from large-scale real-world experience, and steadily increase autonomy and performance beyond human levels. Join a focused team building this system end to end, with significant ownership and meaningful equity.

What you’ll do

• Create and deploy model-based reinforcement learning algorithms
• Partner with researchers specializing in imitation learning and world models

What we look for

• Strong background in deep learning and reinforcement or imitation learning
• Proven ability to implement and debug reinforcement learning algorithms
• Proficiency with Python and deep learning frameworks like JAX or PyTorch
• Publications at premier venues (NeurIPS, ICLR, ICML, CVPR, RSS, CoRL, ICRA), or equivalent public evidence