Lead Research Engineer, Data Quality
Core
Lead a team building systems to evaluate and improve training data quality for reinforcement learning environments and frontier model training.
Role type
Senior technical leadership, Lead Research Engineer
Builds
QC systems, validation pipelines, internal tools, dashboards, and feedback loops for agent data evaluation
Domain
AI infrastructure, Reinforcement Learning, Model Training
Deliverable
production ML models
Required skills
Python, Docker, Linux, team leadership, experimental design, metrics design, synthetic data validation, failure-mode analysis, trajectory auditing, domain expert collaboration
Preferred skills
Early-stage startup experience, independent execution in fast-paced environments
Technologies
Python, Docker, Linux
Responsibilities
Lead the data quality team in building systems to evaluate thousands of tasks across RL environments and synthetic data; Define data quality strategy by building QC systems and designing experiments to grade agent outputs; Develop new methods for validating synthetic data at scale; Partner with research engineers and domain experts to diagnose quality issues; Turn qualitative research insights into production systems and validation pipelines; Mentor research engineers to maintain technical rigor
Seniority
Senior, hands-on IC with leadership responsibilities
