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HUD is building infrastructure for RL training data and evaluations for frontier AI agents, with a marketplace connecting data vendors to frontier labs. The company has raised $16M from top VCs and was part of Y Combinator W25.
As Technical Program Manager, you will own complex, time-sensitive data and evaluation programs end-to-end for frontier AI labs, data vendors, and internal teams. Your core responsibilities include:
- Own data and evaluation programs from initial scoping through production, QA, delivery, and retrospective analysis
- Translate ambiguous technical requests from AI labs and internal teams into clear specifications with defined milestones, owners, dependencies, and acceptance criteria
- Maintain and improve data quality procedures using quantitative signals and qualitative inspection, analyzing task coverage, difficulty, and reward distributions
- Manage external vendors throughout the delivery lifecycle and maintain scalable delivery pipelines
- Streamline logistics behind data production by improving workflows, documentation, automation, and cross-functional handoffs
- Partner with research, platform, and marketplace teams to translate learnings into product improvements
The team is ~25 people, mostly full-time in-person but 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. HUD has 8 figures in funding and is scaling profitably to meet strong demand.
Full-time employment with offices in San Francisco or Singapore. Remote candidates welcome if they can maintain 70-80% time zone overlap with either office. Visa sponsorship and relocation support provided for strong candidates.
REQUIREMENTS:
- Experience owning complex technical programs in AI/ML, data operations, support engineering, applied research, forward-deployed engineering, or similarly cross-functional environments
- Strong quantitative and qualitative judgment about data—ability to move between aggregate metrics and individual examples to assess dataset/evaluation utility, reliability, and representativeness
- Experience maintaining and improving complex processes, especially in project delivery or data contexts
- Demonstrated ability to bring structure to ambiguous problems, maintain momentum as requirements change, and make sound tradeoffs without waiting for perfect specifications
- Strong written and verbal communication skills to translate messy technical information into clear decisions, owners, and next steps
STRONG ADDITIONAL QUALIFICATIONS:
- Experience with RLHF, reinforcement-learning environments, agent evaluations, human-in-the-loop data pipelines, annotation, or expert data collection
- Experience supporting frontier AI labs, technical enterprise customers, or research teams with urgent and evolving requirements
- Early-stage startup experience with ability to work independently in fast-paced environments
The company prioritizes technical aptitude and learning potential over years of experience and encourages motivated candidates to apply even if they don't meet all criteria.
