SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 135,000 - 230,000 / annual
HUD is building infrastructure for AI data, with a mission to make data valuable for creators and trustworthy for AI labs. The company operates a marketplace and platform used by startups, Fortune 500 companies, and frontier labs. Backed by $16M in funding from top VCs and Y Combinator (W25), HUD is a rapidly growing team of researchers, engineers, and operators.
As a Research Engineer on the Robotics team, you will develop datasets and evaluations that enable training and evaluation of embodied AI systems. Your work will bridge research needs and practical data specifications, ensuring robotics data is both useful and reliable.
Key responsibilities include:
- Researching data needs for robot learning and physical AI systems, translating them into concrete dataset and evaluation specifications
- Defining data schemas, annotations, ground truth, and quality standards for 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, as well as data vendors and buyers, to improve robotics data offerings
The team is approximately 25 people, mostly full-time in-person, with some remote flexibility. The company includes 4 International Olympiad medalists, serial AI startup founders, and researchers with publications at top venues like ICLR and NeurIPS.
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 candidates may also have:
- 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
- Background working 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.
