At Rhoda AI, we're building the full-stack foundation for the next generation of humanoid robots — from high-performance, software-defined hardware to the foundational models and video world models that control it. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling scenarios unseen in training. We work at the intersection of large-scale learning, robotics, and systems, with a research team that includes researchers from Stanford, Berkeley, Harvard, and beyond. We're not building a feature; we're building a new computing platform for physical work — and with over $400M raised, we're investing aggressively in the R&D, hardware development, and manufacturing scale-up to make that 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