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Active
On-site
Mountain View, California, United States
Posted · 11.09.2026
Ashby (US)

# Research Evaluation Lead

Rhoda AI

At Rhoda AI, we’re building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality.
MISSION
Own end-to-end robot model evaluation for Research. Turn research questions into consistent, high-quality, repeatable evals that enable fast iteration and trusted results.
RESPONSIBILITIES
• Translate research intent into clear eval protocols, trial plans, and success criteria.
• Own execution end-to-end: model handoff → station readiness → pilot execution → QA → results.
• Train and manage eval pilots; ensure consistency across people, shifts, and stations.
• Distinguish model failures from hardware, setup, operator, or data-quality issues.
• Maintain eval setups, resets, randomization, metadata, and experiment traceability.
• Track quality, throughput, and bottlenecks; continuously improve the eval process.
• Partner closely with Research, Robot Data, and Eval Platform teams.
WHAT WE’RE LOOKING FOR
• Some understanding of robotics / ML experimentation.
• Computer science background or hands-on experience with coding
• Rigorous, detail-oriented, and able to understand the intent behind an experiment, not just execute instructions.
• Strong hands-on execution and ownership.
• Experience in robotics testing, data collection, lab operations, or QA preferred.
SUCCESS LOOKS LIKE
A researcher can hand off a model and research question and receive a trusted, standardized eval result with sufficient trials and QA.

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