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.
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