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.