Research Engineer, Robot Learning

Relari, inc.; San Francisco, Northern (CA, KY); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Relari, inc. seeks a researcher to advance robotics foundation models, training pipelines, evaluation loops, and real-world robot deployment. You will build representations from multimodal data, train models, and use failures to refine ideas.

The role focuses on depth in machine learning or robot learning, applying Python-based tooling (PyTorch, JAX) to design experiments and measure progress on physical robots. Collaborative, hands-on environment values robust software for robotics experiments.

Qualifications

• Strong foundations in ML or robot learning.
• Experience building and evaluating learning systems in Python (PyTorch, JAX).
• Ability to turn open-ended questions into working experiments with clear conclusions.

Responsibilities

• Own pre-train, post-train, and evaluation pipelines of robotics foundation models.
• Develop vision-language-action models, video models, and world models.
• Investigate representations for transferring skills across robot embodiments.
• Build large-scale training, simulation, and real-world evaluation loops.
• Deploy policies on physical robots and diagnose failures across data, perception, learning, control.
• Improve tooling for data pipelines, teleoperation, retargeting, visualization, and runtime software.
• Read literature, reproduce ideas, and design focused experiments around results.

Skills

Machine learning foundations

Robot learning

Computer vision

Python

PyTorch

JAX

Tools

PyTorch

JAX

Python

Job description

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.