Lead Research Engineer, Data Quality

Responsibilities

• Lead the data quality team in building systems that evaluate tasks across RL environments, synthetic data, benchmarks, and domain-specific workflows.
• Define data quality strategy through QC systems, standards, and experiments that grade agent outputs.
• Develop 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.
• Build internal tools, dashboards, validation pipelines, and feedback loops that turn research insights into production systems.
• Mentor research engineers and maintain high standards for technical rigor, clarity, and execution speed.

Requirements

• 5+ years of relevant engineering or research experience building systems for AI/ML data evaluation or data quality.
• Demonstrated experience leading technical teams through ambiguous projects from definition to implementation and iteration.
• Advanced proficiency in Python, Docker, and Linux environments.
• Experience with QC systems, evaluations, benchmarks, synthetic data pipelines, validation workflows, or model evaluation infrastructure.
• Research-oriented understanding of AI evaluations and post-training beyond surface-level agent tooling projects.
• Ability to design metrics, experiments, and QA/QC processes.
• Strong written communication and ability to explain methodology to diverse audiences.
• Experience working with subject-matter experts to convert domain judgment into scalable review or generation systems.
• Early-stage startup experience and ability to work independently in fast-paced environments.

Benefits

• Salary range is $150,000 to $180,000 USD annually.
• Visa sponsorship is available.
• On-site role in San Francisco, California, USA; the team also has a presence in Singapore.