Research Engineer, Robotics Data

HUD is building infrastructure to create RL training data and evals for frontier AI agents, as well as a marketplace to sell these to frontier labs through the HUD marketplace. Our platform is used by frontier labs, Fortune 500 companies, and startups. We’ve raised $16M from top VCs and were YC W25.

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.

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

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

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

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

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

• Experience building research tools or pipelines without a fully prescribed roadmap

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

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

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

• Early-stage startup experience and strong communication skills for collaboration across teams and time zones

We prioritize technical aptitude and learning potential over years of experience. Motivated candidates are encouraged to apply even if they don't meet all criteria.