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.