Lead Research Engineer, Data Quality
Core
Lead the data quality team to shape how training data is assessed and improved for reinforcement learning and model evaluation, helping research teams create reliable AI agents.
Role type
Senior IC lead research engineer (data quality)
Builds
Systems that evaluate tasks across reinforcement learning environments, synthetic data, benchmarks, and domain-specific workflows
Domain
AI/ML infrastructure, reinforcement learning, synthetic data
Deliverable
production ML models
Required skills
Python, Docker, Linux, AI evaluations, post-training, benchmarks, synthetic data pipelines, model evaluation infrastructure, metrics design, experiment design, quality processes
Preferred skills
Leading technical projects through ambiguous problem definition, translating research findings into production systems, working with subject-matter experts, early-stage startup experience
Technologies
Python, Docker, Linux
Responsibilities
Lead development of systems that evaluate tasks across reinforcement learning environments, synthetic data, benchmarks, and domain-specific workflows; Set data quality strategy, establish standards, and design experiments to assess agent outputs; Develop scalable methods for validating synthetic data, including failure analysis, task mutation checks, and trajectory audits; Work with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation; Turn research insights into production tools, dashboards, validation pipelines, and feedback loops; Mentor research engineers and promote technical rigor, clear communication, and strong execution
