Robotics ML Research Engineer – Dexterous Skill Learning

Socket.dev; San Francisco (CA); On-site; USD 130,000 - 190,000; Full time

Job summary

Relari is a small research and engineering startup building new ways for robots to learn dexterous skills from human biomechanics. You will work directly with the founders, own problems end to end, and test ideas on real robotic systems. This full-time, on-site role is in San Francisco.

You will develop and evaluate learning systems, deploy policies on physical robots, and improve data pipelines and tooling to advance research progress with measurable results.

Qualifications

• Experience building and evaluating learning systems in Python using PyTorch or JAX.
• Evidence you can turn open-ended technical questions into working experiments with clear conclusions.
• Comfort debugging real data and physical systems, not only benchmarks.

Responsibilities

• Own pre-train, post-train, and evaluation pipelines of robotics foundation models from diverse multimodal datasets.
• 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.
• Deploy policies on physical robots and diagnose failures across data, perception, learning, control, and hardware.
• Improve tooling: data pipelines, teleoperation, retargeting, visualization, and robot runtime software.

Skills

Machine learning

Robot learning

Computer vision

Python

PyTorch

Experiment design

Tools

Python

PyTorch

JAX

TensorFlow

Job description

Relari is a small research and engineering startup building new ways for robots to learn dexterous skills from human biomechanics. You will work directly with the founders, own problems end to end, and test ideas on real robotic systems. This full-time, on-site role is in San Francisco.

You will develop and evaluate learning systems, deploy policies on physical robots, and improve data pipelines and tooling to advance research progress with measurable results.