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About the Role

As a Research Scientist / Engineer — Data, you will develop methods to efficiently transform diverse data into improved humanoid robot policies. Your research will investigate data's impact on robot learning, its utility, and optimal introduction during training, utilizing on-robot teleoperation, simulation, and off-robot data. Your findings will directly influence data collection and acquisition strategies, enhancing robot capabilities and optimizing resource utilization.

Responsibilities

Develop methods to make off-robot data useful for robot learning, including action inference, retargeting, and alignment across viewpoints and embodiments.
Research data composition across training stages: pretraining, mid-training, and post-training using demonstrations, simulation, and deployment experience.
Design data curation tools and selection methods for filtering, segmentation, annotation, quality scoring, and identifying informative examples.
Study source mixtures, sampling strategies, curricula, supervision signals, re-annotation and data ingestion strategies, and implement these ideas in training experiments.
Run controlled ablations and scaling studies to isolate the effects of data quantity, quality, diversity, and source under realistic compute budgets.
Measure impact on real-robot success, generalization, robustness, and adaptation efficiency.
Shape collection and acquisition priorities based on evidence.
Work with data engineers to turn validated research methods into reproducible processing and training workflows.

Requirements

Strong experience in data-centric research, robot learning, computer vision, or multimodal learning.
Strong Python and PyTorch skills with hands-on experience implementing research ideas and training or fine-tuning models.
Solid understanding of machine learning and experimental design, including meaningful baselines, controlled comparisons, reproducibility, and evaluation beyond training loss.
Experience investigating how data quality, coverage, mixtures, and distribution shifts affect downstream model behavior.
Experience with multimodal or sequential data, such as video, motion, sensor streams, or action trajectories.
Ability to connect research findings to practical decisions about data collection, training, and robot evaluation.
Experience working with large-scale multimodal data.
Ability to thrive in a hands-on, fast-paced startup environment: autonomous, rigorous, collaborative, and curious about robotics and learning.

Skills

Data-centric research
Robot learning
Computer vision
Multimodal learning
Python
PyTorch
Machine learning
Experimental design
Data quality analysis
Data coverage analysis
Distribution shift analysis
Multimodal data handling
Sequential data handling
Video analysis
Motion analysis
Sensor stream analysis
Action trajectory analysis
Data collection strategy
Data training strategy
Robot evaluation
Large-scale multimodal data
Egocentric data
Robot teleoperation
UMI-style demonstrations
Simulation
Autonomous-vehicle datasets
3D geometry
Pose estimation
Retargeting
Cross-embodiment learning
Imitation learning
Reinforcement learning
Sim-to-real transfer
Active data selection
Public datasets
Open-source contributions
Technical writing
Data ingestion pipeline optimization

About the Company

UMA is an inclusive workplace that values exceptional researchers and builders over perfect resumes.