Research Engineer/Scientist, World Models Research · San Francisco · Full-time →

Tau Robotics, Inc.; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Tau Robotics, Inc. seeks a Research Engineer/Scientist to build world models for autonomous robots and advance robot intelligence.

You will develop algorithms and collaborate with researchers to train foundation models, applying deep learning to real-world robotic data. Candidates should demonstrate strong Python proficiency and a track record in large generative models, with publications or public evidence in top venues.

Qualifications

• Strong background in deep learning research with focus on large generative models.
• Experience implementing learning algorithms for video or image generation.
• Proficiency in Python and deep learning libraries (PyTorch or JAX).
• Publications at top venues or equivalent public evidence.

Responsibilities

• Develop and implement new algorithms for training world models.
• Collaborate with researchers to train robot foundation models.

Skills

Generative models

Video/image generation

Python

Publications

Tools

PyTorch

JAX

Job description

Research Engineer/Scientist, World Models.

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

• Develop and implement new algorithms for training world models


• Collaborate with robot learning researchers to train robot foundation models

What we look for

• Deep technical knowledge and research experience in large generative models


• Experience implementing learning algorithms for video or image generation


• Extensive Python proficiency and familiarity with deep learning libraries (PyTorch or JAX)


• Publications at top venues (NeurIPS, ICLR, ICML, CVPR, RSS, CoRL, ICRA), or equivalent public evidence