Lead Research Engineer, Data Quality

About the Role

Join an early-stage AI/ML startup building the infrastructure layer for reinforcement learning environments and post-training data at the frontier. As Lead Research Engineer, Data Quality, you will own the strategy and systems that measure, improve, and scale training data for frontier agents. This is a high-impact, hands-on leadership role where you'll shape both the technical direction and internal research culture around what makes agent training data truly useful.

The company is a well-funded, rapidly growing AI infrastructure platform (Series A/B stage) focused on RL environment tooling, synthetic data generation, and model evaluation — working directly with AI labs and research teams.

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 the data quality strategy by building QC systems, enforcing standards, and designing experiments to grade agent outputs.

• Develop new 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.

• Help build internal research intuition around what makes agent training data realistic, learnable, diverse, reliable, and useful — not just superficially correct.

• Mentor other research engineers, maintaining a high bar for technical rigor, clarity, and execution speed.

What We're Looking For

Required

• 5+ years of relevant engineering or research experience.

• Proven track record leading technical teams on ambiguous projects from problem definition through implementation and iteration.

• Advanced proficiency in Python, Docker, and Linux environments.

• Hands-on experience building QC systems, evals, benchmarks, synthetic data pipelines, validation workflows, or model evaluation infrastructure.

• Deep intuition for data quality — what makes training tasks realistic, learnable, diverse, reliable, and useful.

• Comfort designing metrics, experiments, and QA/QC processes, not just executing them.

• Strong written communication; ability to explain methodology clearly to researchers, engineers, and external audiences.

• Experience working with subject-matter experts to capture domain judgment and convert it into scalable review or generation systems.

• Early-stage startup experience with demonstrated ability to work independently in fast-paced environments.

• Detail-oriented mindset with an eye for subtle inconsistencies or edge cases in data.

Nice to Have

• Background in reinforcement learning, reward modeling, or agent evaluation.

• Experience shipping production research infrastructure (not just prototypes).

• Familiarity with large-scale task execution systems or distributed evaluation pipelines.

Compensation & Benefits

• Salary: $150,000 – $250,000 USD annually, commensurate with experience.

• Equity participation in an early-stage, high-growth AI company.

• Visa sponsorship available.

Location

This is an on-site role based in San Francisco, CA. Candidates should be willing and able to work from the office. Relocation support may be available for strong candidates.