Description review
Research Engineer, Robot Learning
Relari · San Francisco, California, US · back to the listing
HR standards
65/100
needs work
Title ↔ description
82/100
solid
Reads as
Machine Learning Engineer
97% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
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.
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.