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Salary
$125k – $175k per year
Location
In office (San Francisco)
Employment
Full-Time
Overview
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Impact
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Relari pairs video demonstrations with surface electromyography (EMG) to study how human biomechanics can unlock force-aware, dexterous robot manipulation.

About the role

You will work across the complete learning-to-deployment loop: building representations from human data, training models, evaluating them in simulation and on physical robots, and using failures to decide what to try next. Our research spans vision-language-action models, video models, world models, and policies that learn from biomechanical signals. We are looking for depth in machine learning or robot learning-not expertise in every part of the stack-and the willingness to follow an idea all the way to robot performance.

What you’ll do

  • Own pre-train, post-train, and evaluation pipelines of robotics foundation models from diverse multimodal human and robot 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 that make research progress measurable.
  • 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 promising ideas, and design focused experiments around the results.

What we’re looking for

  • Strong foundations in machine learning, robot learning, computer vision, or a closely related field.
  • Experience building and evaluating learning systems in Python using PyTorch, JAX, or similar tools.
  • Evidence that you can turn an open-ended technical question into a working experiment and a clear conclusion.
  • Comfort debugging real data and physical systems rather than working only with clean benchmarks.
  • Experience with VLA models, video models, world models, imitation learning, reinforcement learning, or large-scale model training is useful; we do not expect depth across every area.

Working at Relari

Relari is a small research and engineering startup developing new ways for robots to learn dexterous skills from human biomechanics. Our founders have AI research roots at MIT and NVIDIA, along with autonomous-vehicle and robotics deployment experience at Pony ai and Dexterity. We are backed by top investors including Y Combinator, General Catalyst, and Soma Capital. You will work directly with the founders, own problems end to end, and test your ideas on real robotic systems. This role is full-time and on-site in San Francisco.

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