Research Engineer, QC Automation
Core
Build scalable quality control systems for training data used to train and evaluate AI agents, grounded in human judgment rather than heavy reliance on large language models.
Role type
Research Engineer, QC Automation
Builds
Automated QA/QC pipelines, evaluation metrics, and infrastructure tools for data vendors
Domain
AI infrastructure, Reinforcement Learning, Data Quality
Deliverable
production ML models
Required skills
Python, Docker, Linux, scalable data validation pipelines, end-to-end automated QA/QC systems, reinforcement learning benchmarks, statistics, experiment design
Preferred skills
Human judgment integration, vendor collaboration, rule-based validation, model-assisted validation
Technologies
Python, Docker, Linux
Responsibilities
Automate quality control for training data, define and enforce data quality standards, design experiments and metrics to evaluate agent outputs, work with data vendors to diagnose quality issues, turn findings into sampling strategies and validation pipelines, incorporate insights into infrastructure tools
Seniority
Mid-level, hands-on IC
