Lead Research Engineer, Data Quality
About the role
HUD is building reliable, fair and open infrastructure for AI data. The Lead Research Engineer, Data Quality will own how HUD measures, improves, and scales the quality of training data for frontier agents, leading the data quality team to build systems that evaluate tasks across RL environments, synthetic data, benchmarks, and domain-specific workflows.
Responsibilities
- Lead HUD’s data quality strategy including building QC systems, defining and enforcing quality standards, and designing experiments to grade agent outputs
- Develop new methods for validating synthetic data at scale, such as failure-mode analysis, task mutation checks, and trajectory auditing
- Partner with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation workflows
- Turn qualitative research insights into production systems, internal tools, dashboards, validation pipelines, and feedback loops
- Help build internal research taste around what makes agent training data actually useful, not just superficially correct
- Mentor other research engineers to maintain a high bar for technical rigor, clarity, and execution speed
Qualifications
- Advanced proficiency in Python, Docker, and Linux environments
- Deep intuition for data quality - ability to reason about task realism, learnability, diversity, reliability, and usefulness for training
- Experience building QC systems, evals, benchmarks, synthetic data pipelines, validation workflows, or model evaluation infrastructure
- Comfort working across messy human and technical systems including domain experts, vendors, generated data, model outputs, graders, and infrastructure
- Strong written communication and ability to explain methodology clearly to researchers, engineers, labs, and external audiences
Skills
Benefits
- Competitive compensation
- 100% covered medical, dental, and vision (US and Singapore)
- Lunch and dinner for in-office employees
- Company-wide holiday break plus PTO and paid holidays
- Equinox membership, 401k, commuter benefits (US) and health/wellness stipend (Singapore)
- Unlimited access to tokens for ChatGPT, Claude Code, Cursor, etc.
- Two top-floor offices in San Francisco and Singapore
- Annual travel budget to visit either office
About hud
Platform for building RL environments and evals
HUD (YC W25) is developing agentic evals and RL environments for Computer Use Agents (CUAs) that browse the web for frontier AI labs. Our CUA Evals framework is the first comprehensive evaluation tool for CUAs. People don't actually know if AI agents are working reliably. To make AI agents work in the real world, we need detailed evals for a huge range of tasks. We're backed by Y Combinator, and work closely with frontier AI labs to provide agent evaluation and training infrastructure at scale.
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