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About the Role
This senior individual-contributor and team-lead role focuses on AI evaluation, synthetic data, and reinforcement learning infrastructure. You will be responsible for the strategy and systems that measure and improve the quality of training data for frontier AI agents, influencing internal research culture around data usefulness.
Responsibilities
- Lead the data quality team in building systems to 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.
Requirements
- 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.
- 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.
Skills
- Python
- Docker
- Linux
- AI/ML data evaluation
- Data quality engineering
- Reinforcement learning infrastructure
- Synthetic data validation
- QC systems
- Evals
- Benchmarks
- Model evaluation infrastructure
- Metrics design
- Experiment design
- QA/QC processes
Location
- On-site, United States
- San Francisco Bay Area preferred
Work Type
- On-site
Experience Level
- Senior
- 5 or more years of experience
Salary/Compensations
- $150,000 to $180,000 per year (USD)
Benefits
- Visa sponsorship available