Lead Research Engineer, Data Quality

hud (YC W25)

. Lead HUD’s data quality strategy, including building QC systems, defining and enforcing quality standards, and designing experiments to grade agent outputs .

Posted 9/23/2026full-timeRemote • California • United States, SingaporeSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates advanced proficiency in Python, Docker, and Linux environments while leading data quality strategies and building QC systems. Capable of designing metrics, experiments, and validation workflows to enhance data generation and ensure high standards of data quality.

Highest-signal resume keywords

Python ProficiencyDocker ExperienceData Quality Strategy LeadershipQC Systems DevelopmentTeam Leadership on Technical Projects

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonDockerLinuxQC SystemsValidation WorkflowsSynthetic Data PipelinesModel Evaluation InfrastructureMetrics DesignExperiment DesignFailure-Mode Analysis

Soft Skills

Strong Written CommunicationDetail-OrientedMentoringCollaboration

Industry Keywords

Data QualitySynthetic DataReinforcement LearningDomain ExpertiseFeedback Loops

Tech Stack

Tools & technologies

DockerLinuxPython

About the role

Key responsibilities & impact

Lead HUD’s data quality strategy, including building QC systems, defining and enforcing quality standards, and designing experiments to grade agent outputs
Develop methods for validating synthetic data at scale, including 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
Build internal research taste around what makes agent training data useful, not just superficially correct
Mentor research engineers to maintain high standards for technical rigor, clarity, and execution speed
Lead the data quality team in evaluating thousands of tasks across RL environments, synthetic data, benchmarks, and domain-specific workflows

Requirements

What you’ll need

Advanced proficiency in Python, Docker, and Linux environments
Deep intuition for data quality, including 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 ability to explain methodology clearly to researchers, engineers, labs, and external audiences
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
Ability to design metrics, experiments, and QA/QC processes
Early-stage startup experience and ability to work independently in fast-paced environments
Detail-oriented with ability to spot subtle inconsistencies or edge cases in data
Ability to work hours that overlap 70–80% with San Francisco or Singapore time zones

Benefits

Comp & perks

100% covered top-of-the-line medical, dental, and vision from Blue Shield of CA (US employees)
Lunch and dinner when you’re in the office (in-office employees)
Company-wide holiday break (Christmas Eve to New Year’s Day) on top of PTO and paid holidays
Equinox membership (US employees)
401k (US employees)
Commuter benefits (US employees)
Unlimited access to tokens for ChatGPT, Claude Code, Cursor, etc.
Support for relocation and visas for strong full-time candidates to the US or Singapore