Join an engineering team building infrastructure for AI reinforcement learning and evaluation. In this role, you will own quality control automation for training data, developing systems that help maintain high standards as data needs grow.
Build automated quality control systems grounded in human judgment and a strong understanding of data, rather than relying heavily on large language models.
Define and enforce quality standards for training data.
Design experiments and metrics to assess agent outputs.
Work with data vendors to investigate quality issues, diagnose agent failure modes, and improve data generation processes.
Turn quality control findings into sampling strategies and rule-based or model-assisted validation pipelines.
Improve infrastructure tools and vendor workflows to reduce anomalies, inconsistencies, and edge cases.
Two to four years of experience in research engineering or a similar role focused on quality control automation.
Proficiency with Python, Docker, and Linux.
Experience building scalable data validation pipelines and end-to-end automated QA or QC systems.
Background in benchmarks and evaluations for reinforcement learning data, including task design, reliable rubrics, and useful training trajectories.
Experience measuring training data quality, designing experiments and metrics, and creating quality control processes.
Strong statistical understanding, communication skills, curiosity, and comfort working independently in unstructured, fast-paced environments.
Annual salary: $100,000 to $170,000 USD. Visa sponsorship is available.
On-site in Singapore, Singapore.
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