Lead Data Quality Engineer for Frontier AI Systems

HUD; San Francisco (CA); Hybrid; USD 180,000 - 260,000; Full time

Benefits offered by this job

Top-tier medical, dental, vision

Lunch & dinner in office

Holiday break

Equinox membership

401k

Commuter benefits

Unlimited AI tokens

Job summary

HUD is seeking a Lead Research Engineer, Data Quality to own how HUD measures, improves, and scales the quality of training data for frontier agents. You’ll lead the data quality team in building systems that evaluate thousands of tasks across RL environments, synthetic data, benchmarks, and domain-specific workflows.

You will mentor engineers, design experiments, and collaborate with domain experts to drive rigorous data generation and evaluation.

Qualifications

• You may be a good fit if you have advanced proficiency in Python, Docker, and Linux environments.
• You have deep intuition for data quality and can reason about task realism, learnability, diversity, reliability, and usefulness for training.
• You have experience building QC systems, evals, benchmarks, synthetic data pipelines, validation workflows, or model evaluation infrastructure.
• You are comfortable working across messy human and technical systems with domain experts, vendors, and infrastructure.
• You have strong written communication and can explain methodology clearly to researchers, engineers, labs, and external audiences.
• Experience leading teams on ambiguous technical projects from definition to implementation is a plus.

Responsibilities

• Lead HUD’s data quality strategy including building QC systems, defining and enforcing quality standards, and designing experiments to grade agent outputs
• Develop new methods for validating synthetic data at scale, such as 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
• Help build internal research taste around what makes agent training data actually useful, not just superficially correct
• Mentor other research engineers to maintain a high bar for technical rigor, clarity, and execution speed

Skills

Python

Docker

Linux

Data quality intuition

QC systems design

Education

Bachelor's degree in a technical field

Tools

Kubernetes

Job description

HUD is seeking a Lead Research Engineer, Data Quality to own how HUD measures, improves, and scales the quality of training data for frontier agents. You’ll lead the data quality team in building systems that evaluate thousands of tasks across RL environments, synthetic data, benchmarks, and domain-specific workflows.

You will mentor engineers, design experiments, and collaborate with domain experts to drive rigorous data generation and evaluation.