Lead Research Engineer, Data Quality
About the Role
This is a senior individual-contributor and team-lead role sitting at the intersection of AI evaluation, synthetic data, and reinforcement learning infrastructure. You will own the strategy and systems that measure and improve training data quality for frontier AI agents, shaping internal research culture around what makes agent data genuinely useful rather than superficially correct.
What You'll Do
Lead the data quality team in building systems that evaluate thousands of tasks across RL environments, synthetic data pipelines, benchmarks, and domain-specific workflows.
Define the 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.
Translate qualitative research insights into production systems: internal tools, dashboards, validation pipelines, and feedback loops.
Build internal research taste around what makes agent training data realistic, learnable, diverse, reliable, and useful.
Mentor other research engineers to maintain a high bar for technical rigor, clarity, and execution speed.
What We're Looking For
5 or more years of experience in research or data quality engineering, specifically building systems for AI/ML data evaluation.
Demonstrated track record of leading technical teams or projects in data quality or AI/ML evaluation, from problem definition through implementation and iteration.
Advanced proficiency in Python, Docker, and Linux environments.
Experience building QC systems, evals, benchmarks, synthetic data pipelines, or model evaluation infrastructure.
Deep, research-oriented understanding of AI evals and post-training, going well beyond surface-level agent harness projects.
Strong intuition for characteristics of high-quality training data and the ability to design metrics, experiments, and QA/QC processes, not just execute them.
Experience collaborating with subject-matter experts to capture domain judgment and convert it into scalable review or generation systems.
Strong written communication skills, with the ability to explain methodology clearly to mixed technical and non-technical audiences.
Comfort operating independently in an early-stage startup environment with ambiguous, fast-moving priorities.
Detail-oriented mindset with a sharp eye for subtle inconsistencies and edge cases in data.
Compensation & Benefits
Salary: $150,000 to $180,000 per year (USD)
Visa sponsorship: available
Location
On-site, United States. Candidates based in or willing to relocate to the San Francisco Bay Area are preferred.