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.