Research Engineer, QC Automation

ABOUT THE ROLE Join an engineering team at an early-stage AI infrastructure company focused on reinforcement learning environments and evaluation. You will build quality-control systems for training data, helping ensure that data and evaluations are reliable as demand grows. WHAT YOU'LL DO - Automate quality control for training data produced using the company's infrastructure. - Define and enforce clear quality standards using human judgment and careful analysis, rather than relying heavily on large language models. - Design experiments and metrics to assess agent outputs and evaluation quality. - Work with data vendors to investigate quality issues, diagnose agent failure modes, and improve data-generation processes. - Turn quality-control findings into auditing systems, sampling strategies, and rule-based or model-assisted validation pipelines. - Improve infrastructure tools and vendor-facing workflows to reduce anomalies, inconsistencies, and edge cases. WHAT WE'RE LOOKING FOR - Two to four years of experience in a research engineering or similar role focused on quality-control automation, plus a degree in a technical field. - Proficiency with Python, Docker, and Linux. - Experience independently building scalable data-validation pipelines and end-to-end automated QA or QC systems. - Background in benchmarks and evaluations for reinforcement learning data, including realistic task design, reliable rubrics, and useful training trajectories. - Experience defining and measuring training-data quality, and designing experiments, metrics, and quality processes. - Strong statistical knowledge and the ability to communicate clearly across time zones and work independently in an unstructured startup environment. COMPENSATION & BENEFITS Base salary: $100,000 to $170,000 USD annually. Visa sponsorship is available. LOCATION On-site in Singapore, Singapore.