ashby
Research Assistant
Generalist
San Mateo, USA
Full-time
Entry
Onsite
USD 150000-200000 yearly / year
Discovered 1 weeks ago
Free
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We are looking for a hands-on Research Assistant to help run real-world experiments at the intersection of robotics, machine learning, and physical-world evaluation.
At Generalist, we are building foundation models for robots. These models improve through a tight feedback loop: collect data, train or fine-tune models, evaluate them in the real world, analyze results, and repeat. This role helps make that loop faster, more rigorous, and more reliable.
You will work closely with ML researchers and robotics engineers to run robot experiments, design evaluation tasks, collect data, measure success rates, and document repeatable workflows. You do not need to be an experienced ML research scientist or robotics engineer, but you should be excited by careful experimentation, physical systems, statistical rigor, and hands-on iteration.
A major part of this role is helping ensure our evaluations are trustworthy. We care deeply about experimental design, controls, sample sizes, variance, repeatability, and avoiding misleading conclusions from noisy real-world robot trials.
Full Job Posting
About the Role:
We are looking for a hands-on Research Assistant to help run real-world experiments at the intersection of robotics, machine learning, and physical-world evaluation.
At Generalist, we are building foundation models for robots. These models improve through a tight feedback loop: collect data, train or fine-tune models, evaluate them in the real world, analyze results, and repeat. This role helps make that loop faster, more rigorous, and more reliable.
You will work closely with ML researchers and robotics engineers to run robot experiments, design evaluation tasks, collect data, measure success rates, and document repeatable workflows. You do not need to be an experienced ML research scientist or robotics engineer, but you should be excited by careful experimentation, physical systems, statistical rigor, and hands-on iteration.
A major part of this role is helping ensure our evaluations are trustworthy. We care deeply about experimental design, controls, sample sizes, variance, repeatability, and avoiding misleading conclusions from noisy real-world robot trials.
You’ll be responsible for:
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