Research Engineer, QC Automation

Location: San Francisco Bay Area, CA — On-site
Employment: Full-time
Compensation: $100,000–$200,000
Visa: Relocation and visa support available for strong candidates
Experience: Technical aptitude and learning potential matter more thanyears of experience.

HUD builds infrastructure for companies creating training data for AI agents. As demand grows, we need robust systems that can maintain and scale data quality.

As a Research Engineer, QC Automation
, you’ll buildthe systems that make that possible. You’ll develop automated quality-control infrastructure grounded in human judgment and a deep understanding of whatmakes training data useful—not simply by relying on LLMs to judge other LLMs.

This is a highly autonomous role for someone who enjoys ambiguous problems, learns quickly, and can turn unclear quality requirements into measurable, scalable systems.

• Build quality-control systems grounded in human judgment and a deep understanding of data quality, with limited reliance on LLM-based evaluation.
• Define,formalize, and enforce quality standards for training data
  .
• Design
  experiments, benchmarks, and metrics to evaluate agent performance.
• Partner with data vendors to identify agent failure modes
  , debug quality issues, and improve data-generation processes.
• Build systems for auditing supplier datasets
  , including sampling strategies, rule-based validation, model-assisted validation, and feedback loops.
• Integrate QC insights into HUD’s infrastructure and data-vendor portal to reduce anomalies,inconsistencies, and edge cases
  .
• Work across unfamiliar domains and turn loosely defined quality problems into reliable,repeatable processes
  .

• Strong proficiency in Python, Docker, and Linux environments.
• Excellent judgment about what constitutes good data and how to measure it.
• Genuine curiosity about unfamiliar domains, with the ability to ask the right questions to develop a deep understanding quickly.
• Experience building scalable data-validation pipelines, automated QA/QC systems,or similar infrastructure without a prescribed roadmap.
• Experience designing or working with benchmarks and evaluations
  .
• Comfort working in an early-stage startup environment
  , taking ownership and executing independently.
• Atrack record of learning quickly and tackling problems that don't comewith clear instructions.

• Knowledge of statistics and experimental design.
• Strong
  written and verbal communication skills.
• Comfort designing metrics, experiments, and QA/QC processes
  .
• Abilityto construct tasks for new evaluations and benchmarks
  .
• Comfort operating in unstructured problem spaces
  .
• Atendency to dig into the underlying problem rather than reaching for thefirst available tool or solution.