Robot Learning Research Engineer - Dexterous Manipulation

Relari; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Relari is a small research and engineering startup in San Francisco focused on teaching robots to learn dexterous skills from human biomechanics. This full-time on-site role involves building and evaluating robotics foundation models, adapting vision-language-action and world models, and deploying policies to real robots.

You will work directly with the founders, own problems end to end, and iterate on experiments that advance robot performance in real hardware environments.

Qualifications

• Strong foundations in machine learning, robot learning, or computer vision.
• Experience building and evaluating learning systems in Python using PyTorch, JAX, or similar tools.
• Ability to turn open-ended technical questions into working experiments with clear conclusions.
• Comfort debugging real data and physical systems rather than only clean benchmarks.
• Experience with VLA models, video models, world models, imitation learning, RL, or large-scale model training is useful.

Responsibilities

• Own pre-train, post-train, and evaluation pipelines of robotics foundation models from diverse multimodal data.
• Develop and adapt vision-language-action models, video models, and world models for dexterous manipulation.
• Investigate representations and training methods for transferring human skills across robot embodiments.
• Build reproducible large-scale training, simulation, and real-world evaluation loops that show progress.
• Deploy policies on physical robots and diagnose failures across data, perception, learning, control, and hardware.
• Improve practical experiment tooling, including data pipelines, teleoperation, retargeting, visualization, and robot runtime software.
• Read relevant research, reproduce ideas, and design focused experiments around results.

Skills

ML

Robot Learning

Computer Vision

Python

Tools

PyTorch

JAX

Job description

Relari is a small research and engineering startup in San Francisco focused on teaching robots to learn dexterous skills from human biomechanics. This full-time on-site role involves building and evaluating robotics foundation models, adapting vision-language-action and world models, and deploying policies to real robots.

You will work directly with the founders, own problems end to end, and iterate on experiments that advance robot performance in real hardware environments.