Research Engineer, QC Automation

About this role

Research Engineer role on an AI infrastructure team focused on building automated quality control systems for reinforcement learning training data. You'll own end-to-end QC pipelines that validate data quality at scale, working with data vendors and designing metrics grounded in human judgment rather than LLM automation.

What you'll do

• Build and maintain automated QC systems for training data validation and quality assurance
• Design experiments, metrics, and rubrics to evaluate agent outputs across diverse RL tasks
• Partner with data vendors to diagnose quality issues, debug failures, and improve data generation processes
• Define quality standards and enforcement mechanisms for AI training data pipelines
• Develop sampling strategies and model-assisted validation pipelines for continuous auditing
• Integrate QC insights into infrastructure tools and vendor portals to reduce edge cases

What they're looking for

• Python
• Docker
• Linux environments
• Data validation and QA/QC systems
• Reinforcement learning benchmarks and evaluation
• Experimental design and metrics creation
• Statistics and data analysis
• Cross-functional communication

Benefits

• Visa sponsorship available
• On-site in San Francisco or Singapore with flexible remote contractor options
• Work on critical infrastructure for AI training at scale
• Autonomous role in early-stage, fast-paced environment