Research Scientist / Engineer - Data

Your Mission

AsResearch Scientist / Engineer — Data, you will develop the methods that efficiently turn diverse data into better humanoid robot policies at UMA. You will investigatewhich data improves robot learning, how to make it useful, and when to introduce it during training.

You will work withon-robot teleoperation, simulation, and off-robot data—including egocentric human recordings and UMI-style demonstrations—acrosspretraining, mid-training, and post-training. Your research will help answer central questions: what should we collect next? How can human demonstrations transfer to robots? Which data mixtures, representations, and supervision signals produce the greatest gains in real-world performance?

We already have a working data and training stack and a strong team behind it. Working closely with our AI, Robotics, and Data Infrastructure teams, you will take ideas from dataset design and research prototypes through controlled training experiments tovalidation on humanoid robots.

Your findings will directly shape ourdata collection and acquisition strategy, helping us improve robot capabilities while making the best and most efficient use of collection effort and compute.

Key responsibilities:

• Develop methods to makeoff-robot data useful for robot learning, including action inference, retargeting, and alignment across viewpoints and embodiments, drawing on camera and hand pose estimation or object tracking where useful.

• Researchdata composition across training stages: broadpretraining,mid-training, andpost-training using demonstrations, simulation, and deployment experience.

• Designdata curation tools and selection methods for filtering, segmentation, annotation, quality scoring, and identifying informative examples.

• Studysource mixtures, sampling strategies, curricula, supervision signals, re-annotation and data ingestion strategies, and implement these ideas in training experiments.

• Run controlledablations and scaling studies to isolate the effects of data quantity, quality, diversity, and source under realistic compute budgets.

• Measure impact onreal-robot success, generalization, robustness, and adaptation efficiency, including how much additional robot data is needed to learn new tasks.

• Shapecollection and acquisition priorities based on evidence: which tasks, environments, embodiments, data types, failures, and recoveries are worth capturing, and at what cost.

• Work with data engineers to turn validated research methods intoreproducible processing and training workflows.

What You Bring to the Table

• Strong experience indata-centric research, robot learning, computer vision, or multimodal learning, demonstrated through research projects, deployed systems, publications, or open-source work.

• StrongPython and PyTorch, with hands-on experience implementing research ideas and training or fine-tuning models.

• A solid understanding ofmachine learning and experimental design: meaningful baselines, controlled comparisons, reproducibility, and evaluation beyond training loss.

• Experience investigating howdata quality, coverage, mixtures, and distribution shifts affect downstream model behavior.

• Experience withmultimodal or sequential data, such as video, motion, sensor streams, or action trajectories.

• The ability to connect research findings to practical decisions about data collection, training, and robot evaluation.

• Experience in working with large-scale multimodal data.

• Thrive in a hands-on, fast-paced startup: autonomous, rigorous, collaborative, and curious about robotics and learning.

• Bonus: egocentric data, robot teleoperation, UMI-style demonstrations, simulation, or autonomous-vehicle datasets.

• Bonus: 3D geometry, pose estimation, retargeting, cross-embodiment learning, imitation learning, reinforcement learning, or sim-to-real transfer.

• Bonus: active data selection, public datasets, open-source contributions, or technical writing.

• Bonus: Optimizing data ingestion pipelines during training.

UMA is an inclusive workplace thatvalues exceptional researchers and builders over perfect resumes. If you enjoy discovering what makes data useful and turning those findings into better robot capabilities, we strongly encourage you to apply even if you don’t tick every box.