Research Engineer
US$100,000 – US$230,000 / yr base salary
Posted 13 Jul 2026
Advertised as “Research Engineer, QC Automation”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
- Visa sponsorship
- Offered
| Base salary | US$100,000 – US$230,000 / yr |
|---|
About HUD
HUD's mission is to build reliable, fair and open infrastructure for AI data. We want data to be valuable for the people who create it and trustworthy for the labs that train on it. Our team is a quickly growing group of researchers, engineers and operators building the economy that shapes what AI will become. Backed by $16M from top VCs and YC (W25), our marketplace and platform are used by startups, Fortune 500 companies and frontier labs.
About the role
We're looking for Research Engineers to automate QC for training data created by companies using HUD’s infrastructure. You’ll build the systems that scale quality to help us meet our continued strong demand.
Responsibilities
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Create QC systems based on true understanding and human judgement, without relying heavily on LLMs
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Define and enforce quality standards for training data
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Design experiments and metrics to grade agent outputs
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Partner with data vendors to debug quality issues and diagnose agent failure modes, provide actionable feedback, and improve their data generation processes
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Translate QC learnings into systems for auditing supplier-generated datasets, including sampling strategies, validation pipelines (rule-based and model-assisted), and feedback loops
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Continuously integrate QC learnings into infrastructure tools and data vendor portal to reduce anomalies, inconsistencies, and edge cases
Experience
You may be a good fit if you have:
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Proficiency in Python, Docker, and Linux environments
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Strong understanding of what “good data” means and how to measure it
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Genuine curiosity of different domains and great at asking questions to understand them
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Built scalable data validation pipelines and automated QA/QC systems end-to-end without a fully prescribed roadmap
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Experience working on benchmarks and evals - you can reason about what makes a task realistic, a rubric reliable, an environment usable, and a trajectory useful for RL training
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Early-stage startup experience with ability to work independently in fast-paced environments
Strong candidates may also: