Research Engineer, QC Automation

San Francisco. Posted 4 months ago

The role

Research Engineers will automate QC for training data created by companies using HUD's infrastructure. You will build systems that scale quality to meet demand.

What you will do

Create QC systems based on true understanding and human judgement
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
Translate QC learnings into systems for auditing supplier-generated datasets
Integrate QC learnings into infrastructure tools and data vendor portal

Required qualifications

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 great at asking questions to understand them
Built scalable data validation pipelines and automated QA/QC systems end-to-end without a fully prescribed roadmap
Experience working on benchmarks and evals

Nice to have

Knowledge of statistics
Strong written and verbal communication skills
Be comfortable designing metrics, experiments, and QA/QC processes
Experience with existing benchmarks and can reason about how to construct tasks in new evals
Thrive in unstructured problem spaces

Benefits

Competitive compensation
100% covered top-of-the-line medical, dental, and vision (US and Singapore employees)
Lunch and dinner for in-office employees
Company-wide holiday break (Christmas Eve to New Year’s Day) on top of PTO and paid holidays
401k with a 4% match
Equinox membership and commuter benefits (US employees)
Health/wellness stipend (Singapore employees)
Unlimited access to tokens for ChatGPT, Claude Code, Cursor, etc.
Annual travel budget to visit either office

Before you apply

We provide support for relocation and visas for strong full-time candidates to the US or Singapore
The process is 2 technical interviews and a 2-3 day work trial

About the company

HUD's mission is to build reliable, fair and open infrastructure for AI data. The team is a growing group of researchers, engineers and operators building the economy that shapes what AI will become.