Lead Research Engineer, Data Quality
About the Role
This is a senior, hands-on leadership role at an early-stage AI/ML startup building infrastructure for reinforcement learning environments and post-training data. You'll own the strategy and systems that measure, improve, and scale training data quality for frontier agents - shaping both the technical direction and the internal culture around what makes agent training data genuinely useful.
What You'll Do
Lead the data quality team in building systems that evaluate thousands of tasks across RL environments, synthetic data, benchmarks, and domain-specific workflows.
Define data quality strategy by building QC systems, enforcing standards, and designing experiments to grade agent outputs.
Develop and implement methods for validating synthetic data at scale, including 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.
Mentor research engineers to maintain a high bar for technical rigor, clarity, and execution speed.
What We're Looking For
5+ years of experience in research or data quality engineering, specifically building systems for AI/ML data evaluation.
Demonstrated track record leading technical teams or projects on ambiguous problems, from definition through implementation and iteration.
Advanced proficiency in Python, Docker, and Linux environments.
Deep intuition for what makes AI agent training data realistic, learnable, diverse, reliable, and useful.
Experience translating research insights into production-grade tools and pipelines.
Ability to design metrics, experiments, and QA/QC processes - not just execute them.
Experience working with subject-matter experts to capture domain judgment and convert it into scalable review or generation systems.
Strong written communication skills; able to explain methodology clearly to technical and non-technical audiences alike.
Comfort navigating complex systems involving domain experts, vendors, model outputs, graders, and infrastructure.
Prior early-stage startup experience; self-directed and effective in fast-paced, resource-constrained environments.
Compensation & Benefits
Base salary $150,000 - $250,000 USD annually. Visa sponsorship is available.
Location
On-site in San Francisco, CA. This role is not remote.