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HUD is building infrastructure to create reinforcement learning training data and evaluations for frontier AI agents, with a marketplace connecting data providers to frontier labs. The company has raised $16M from top VCs and was part of Y Combinator W25.
As a Research Engineer on the Robotics team, you will develop datasets and evaluations that make robotics data useful for training and evaluating embodied AI systems. You'll translate open-ended research needs into concrete data specifications, build validation methods, and run experiments to understand how data quality and structure impact model performance.
Key responsibilities include:
- Researching data needs of robot learning and physical AI systems, converting them into dataset and evaluation specifications
- Defining data schemas, annotations, ground truth, and quality standards across robotics data types
- Designing collection and review protocols that external data providers can execute reliably
- Building tools and validation workflows to audit datasets, identify quality issues, and provide actionable feedback to providers
- Running experiments and analyzing model behavior to understand how data quality, coverage, and structure affect performance
- Collaborating with HUD's research and engineering teams, data vendors, and buyers to improve robotics data offerings
The team is ~25 people, mostly full-time in-person but with some remote flexibility. The team includes 4 International Olympiad medalists, serial AI startup founders, and researchers with publications at top venues like ICLR and NeurIPS. The company is scaling profitably with strong demand.
The company offers competitive compensation, 100% covered top-tier medical/dental/vision (US employees), office meals, company-wide holiday break, Equinox membership, 401k, commuter benefits, and unlimited access to AI tools (ChatGPT, Claude, Cursor tokens).
REQUIREMENTS:
- 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 ability to spot subtle errors, coverage gaps, and failure modes in complex data
- Experience building research tools or pipelines without a fully prescribed roadmap
STRONG ADDITIONAL QUALIFICATIONS:
- Experience with robot trajectories, demonstrations, video, sensor data, simulation, or other multimodal robotics datasets
- Experience with imitation learning, reinforcement learning, or vision-language-action models
- Experience in unstructured problem spaces with ownership from early research through production deployment
- Early-stage startup experience and strong communication skills for cross-team and cross-timezone collaboration
The company prioritizes technical aptitude and learning potential over years of experience. Visa sponsorship and relocation support available for strong candidates to US or Singapore offices. Hiring timeline is rolling with 2 technical interviews and a 2-3 day work trial.
