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Research Scientist / Engineer - Data

UMA· Data
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Paris, FranceFull-timePosted 4d ago

About the role

Research Scientist / Engineer focusing on data-centric methods to improve humanoid robot learning at UMA, through analysis of which data improves learning, how to make data useful, and when to introduce it during training across pretraining, mid-training, and post-training.

Responsibilities

  • Develop methods to make off-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.
  • Research data composition across training stages: broad 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 ablutions 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, including how much additional robot data is needed to learn new tasks.
  • Shape collection 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 into reproducible processing and training workflows.

Qualifications

  • Strong experience in data-centric research, robot learning, computer vision, or multimodal learning, demonstrated through research projects, deployed systems, publications, or open-source work.
  • Strong Python and PyTorch, with hands-on experience implementing research ideas and training or fine-tuning models.
  • Solid understanding of machine learning and experimental design: 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.
  • 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.

Skills

Required:data-centric researchPythonPyTorchmachine learningmultimodal data
Last updated Oct 22, 2026. Interested? Apply on the company site.

About UMA

UMA - Building intelligent robots that enhance quality of life for everyone

At UMA, we develop AI and robotics dedicated to shaping a new economic and societal era. We design general-purpose mobile and humanoid robots with human-level dexterity and a deep understanding of the physical world.

AIBuilding the futureDeep LearningGeneral-purpose robotsLeRobotRoboticsheadline-ai-europe-100-2026intelligent-hardware
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