Research Engineer, Robotics Data

About the role

About HUD

HUD's mission is to build reliable, fair and open infrastructure for AI data. We want data to be valuable for the people who create it and trustworthy for the labs that train on it. Our team is a quickly growing group of researchers, engineers and operators building the economy that shapes what AI will become. Backed by $16M from top VCs and YC (W25), our marketplace and platform are used by startups, Fortune 500 companies and frontier labs.

About the role

We’re looking for a Research Engineer for our Robotics team to develop the datasets and evals that make robotics data useful for training and evaluating embodied AI systems. You’ll translate open-ended research needs into data specifications, build methods to validate data, and run experiments to understand which data and structures improve model performance.

Responsibilities

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Research the data needs of robot learning and physical AI systems, and turn them into concrete dataset and evaluation specifications

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Define data schemas, annotations, ground truth, and quality standards across robotics data types

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Design collection and review protocols that external data providers can execute reliably

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Build tools and validation workflows to audit datasets, identify quality issues, and give providers actionable feedback

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Run experiments and analyze model behavior to understand how data quality, coverage, and structure affect performance

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Work with HUD’s research and engineering teams, and when needed with data vendors and buyers, to improve robotics data offerings

Experience

You may be a good fit if you have:

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Experience in robotics, robot learning, embodied AI, or closely related multimodal research

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Proficiency in Python and experience building data processing, analysis, or evaluation tools

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Experience turning research questions into dataset specifications, experiments, and measurable quality criteria

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Strong understanding of what makes robotics data useful for training or evaluation—and where it can be misleading

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Attention to detail and the ability to spot subtle errors, coverage gaps, and failure modes in complex data

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Experience building research tools or pipelines without a fully prescribed roadmap

Strong candidates may also have:

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Worked with robot trajectories, demonstrations, video, sensor data, simulation, or other multimodal robotics datasets

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Experience with imitation learning, reinforcement learning, or vision-language-action models

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Worked in unstructured problem spaces and take ownership from early research through production deployment