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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 $400M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality.
We're looking for a Research Scientist or Research Engineer to own the strategy and systems for collecting, curating, and scaling high-quality robot learning data. This role sits at the intersection of robotics, data collection, and research — your work directly determines the diversity and quality of the demonstrations our models train on.
What You'll Do
Design and implement teleoperation and demonstration collection systems for high-quality robot learning data
Develop data quality metrics, curation pipelines, and filtering strategies specific to robotic interaction data
Research methods to augment real robot data with synthetic, simulated, or cross-embodiment sources
Identify and source external robotic datasets to expand training diversity across platforms and tasks
Build tooling for researchers to explore, annotate, and iterate on robotic datasets
Collaborate with pre-training and post-training teams to translate model data needs into concrete collection strategies
Measure the downstream impact of data collection decisions on model and policy performance
What We're Looking For
Hands-on experience with robotic data collection, teleoperation systems, or demonstration frameworks
Understanding of what makes robot learning data useful: diversity, coverage, temporal quality, and action fidelity
Strong software engineering skills for building reliable data collection and processing systems
Ability to reason across hardware, pipelines, and model performance
Experience working with real robotic hardware in a research or industrial setting
Nice to Have (But Not Required)
Experience with sim-to-real transfer and synthetic data generation for robotics
Familiarity with cross-embodiment datasets (e.g., Open X-Embodiment, DROID)
Experience with VR teleoperation, motion capture, or dexterous demonstration collection
Understanding of imitation learning and how data properties affect policy generalization
PhD or strong research background in robotics or ML
Why This Role
The data you collect and curate is the direct upstream dependency for all model quality
Unique leverage: improvements to data quality compound across every training run
Work across hardware, systems, and research in a way few roles allow
Direct feedback loop with both robot operators and research scientists to continuously improve data quality
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