Research Engineer, QC Automation

hud (YC W25)

. Build systems to automate quality control for training data created using HUD’s infrastructure .

Posted 9/23/2026full-timeRemote • California • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates proficiency in Python, Docker, and Linux environments while building scalable data validation pipelines and automated QA/QC systems. Capable of defining quality standards and designing metrics and experiments to ensure high-quality training data.

Highest-signal resume keywords

Python ProficiencyDocker ExperienceLinux Environment KnowledgeData Validation Pipeline DevelopmentQuality Control System Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ValidationQuality ControlMetrics DesignExperiment DesignStatistical Knowledge

Soft Skills

Strong Communication SkillsCuriosityIndependent Work AbilityProblem-Solving

Industry Keywords

Training DataQuality StandardsBenchmarkingEvaluationData Vendor Management

Tech Stack

Tools & technologies

DockerLinuxPython

About the role

Key responsibilities & impact

Build systems to automate quality control for training data created using HUD’s infrastructure
Create QC systems based on true understanding and human judgment, without heavy reliance on LLMs
Define and enforce quality standards for training data
Design experiments and metrics to grade agent outputs
Partner with data vendors to debug quality issues and diagnose agent failure modes
Provide actionable feedback and improve vendor data-generation processes
Translate QC learnings into supplier-dataset auditing systems, including sampling strategies, validation pipelines, and feedback loops
Integrate QC learnings into infrastructure tools and the data vendor portal to reduce anomalies, inconsistencies, and edge cases
Participate in a process involving two technical interviews and a 2–3 day work trial

Requirements

What you’ll need

Proficiency in Python, Docker, and Linux environments
Strong understanding of what “good data” means and how to measure it
Genuine curiosity of different domains and ability to ask questions to understand them
Experience building scalable data validation pipelines and automated QA/QC systems end-to-end without a fully prescribed roadmap
Experience working on benchmarks and evals, including reasoning about realistic tasks, reliable rubrics, usable environments, and useful RL training trajectories
Early-stage startup experience
Ability to work independently in fast-paced environments
Knowledge of statistics may be beneficial
Strong written and verbal communication skills may be beneficial
Comfort designing metrics, experiments, and QA/QC processes may be beneficial
Experience with existing benchmarks and constructing tasks in new evals may be beneficial
Ability to thrive in unstructured problem spaces
Ability to work hours that overlap 70–80% with either San Francisco or Singapore time zones for remote work
Technical aptitude and learning potential prioritized over years of experience

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
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