Were looking for a Lead Research Engineer Data Quality to own how HUD measures improves and scales the quality of training data for frontier agents. Youll lead the data quality team in building the systems that evaluate thousands of tasks across RL environments synthetic data benchmarks and domain-specific workflows.
Responsibilities
Lead HUDs 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
Experience
You may be a good fit if you have:
Advanced proficiency in Python Docker and Linux environments
Deep intuition for data quality - you can reason about what makes tasks realistic learnable diverse reliable and useful 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 the ability to explain methodology clearly to researchers engineers labs and external audiences
Strong candidates may also have:
Experience leading teams on ambiguous technical projects from problem definition through implementation and iteration
Experience working with subject-matter experts to capture domain judgment and convert it into scalable review or generation systems
Be comfortable designing metrics experiments and QA/QC processes not just executing them
Early-stage startup experience with ability to work independently in fast-paced environments
Be detail-oriented and able to spot subtle inconsistencies or edge cases in data
Team & company details
Team Size: 15 people currently mostly full-time in-person but some remote.
Our team: Our team includes 4 International Olympiad medalists (IOI ILO IPhO) serial AI startup founders and researchers with publications at ICLR NeurIPS etc.
Company stage: We have 8 figures in funding and high revenue growth. Were scaling profitably and quickly to meet very strong demand.
Logistics
Employment: Full-time.
Location: On-site only for now. You can join the team in the San Francisco Bay Area or Singapore offices.
Visa Sponsorship: We provide support for relocation and visas for strong full-time candidates to the US or Singapore.
Timeline: Applications are rolling. The process is 2 technical interviews and a 2-3 day work trial.
What we offer
Competitive compensation
100% covered top-of-the-line medical dental and vision from Blue Shield of CA (US employees)
Lunch and dinner when youre in the office
Company-wide holiday break (Christmas Eve to New Years Day) on top of PTO and paid holidays
Other perks including an Equinox membership 401k and commuter benefits (US employees)
Unlimited* access to tokens for ChatGPT Claude Code Cursor etc. *By unlimited we mean no one on our token usage leaderboard has ever hit a limit. So we have no idea what the limit is.
About HUDHUD 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. Weve raised $16M from top VCs and were YC...
Were looking for a Lead Research Engineer Data Quality to own how HUD measures improves and scales the quality of training data for frontier agents. Youll lead the data quality team in building the systems that evaluate thousands of tasks across RL environments synthetic data benchmarks and domain-specific workflows.
Responsibilities
Lead HUDs 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
Experience
You may be a good fit if you have:
Advanced proficiency in Python Docker and Linux environments
Deep intuition for data quality - you can reason about what makes tasks realistic learnable diverse reliable and useful 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 the ability to explain methodology clearly to researchers engineers labs and external audiences
Strong candidates may also have:
Experience leading teams on ambiguous technical projects from problem definition through implementation and iteration
Experience working with subject-matter experts to capture domain judgment and convert it into scalable review or generation systems
Be comfortable designing metrics experiments and QA/QC processes not just executing them
Early-stage startup experience with ability to work independently in fast-paced environments
Be detail-oriented and able to spot subtle inconsistencies or edge cases in data
Team & company details
Team Size: 15 people currently mostly full-time in-person but some remote.
Our team: Our team includes 4 International Olympiad medalists (IOI ILO IPhO) serial AI startup founders and researchers with publications at ICLR NeurIPS etc.
Company stage: We have 8 figures in funding and high revenue growth. Were scaling profitably and quickly to meet very strong demand.
Logistics
Employment: Full-time.
Location: On-site only for now. You can join the team in the San Francisco Bay Area or Singapore offices.
Visa Sponsorship: We provide support for relocation and visas for strong full-time candidates to the US or Singapore.
Timeline: Applications are rolling. The process is 2 technical interviews and a 2-3 day work trial.
What we offer
Competitive compensation
100% covered top-of-the-line medical dental and vision from Blue Shield of CA (US employees)
Lunch and dinner when youre in the office
Company-wide holiday break (Christmas Eve to New Years Day) on top of PTO and paid holidays
Other perks including an Equinox membership 401k and commuter benefits (US employees)
Unlimited* access to tokens for ChatGPT Claude Code Cursor etc. *By unlimited we mean no one on our token usage leaderboard has ever hit a limit. So we have no idea what the limit is.
Job Location:
Monthly Salary:
Posted on:
Vacancies:
