ABOUT THE ROLE Join an early-stage AI evaluation infrastructure startup as a Research Engineer focused on quality control automation. You will help build systems that make high-quality training data more scalable, combining careful human judgment with reliable measurement and automation. WHAT YOU'LL DO - Build and improve quality control systems for training data created within reinforcement learning environments. - Define quality standards and design experiments and metrics to assess agent outputs. - Develop auditing approaches, including sampling strategies and rule-based or model-assisted validation pipelines. - Work with data vendors to diagnose quality issues, understand agent failure modes, and improve data generation processes. - Turn quality control findings into improvements to internal infrastructure and vendor-facing tools.
WHAT WE'RE LOOKING FOR - Two to four years of experience in a research engineering or similar role, including hands-on work in quality control automation. - Proficiency with Python, Docker, and Linux, plus experience building scalable data validation and automated QA/QC systems end to end. - Experience with benchmarks and evaluations for reinforcement learning training data, including realistic tasks, reliable rubrics, and useful training trajectories. - Ability to define and measure training data quality, and to build quality control systems grounded in human judgment rather than heavy reliance on large language models. - Knowledge of statistics and experience designing metrics, experiments, and quality assurance processes. - Strong communication skills, curiosity, and the ability to make progress independently in ambiguous, fast-moving environments.
COMPENSATION & BENEFITS Annual salary range: $100,000 to $170,000 USD. Visa sponsorship is available. LOCATION On-site in Singapore. Fully remote independent contractor arrangements may be considered for candidates based elsewhere, particularly in Europe.
Job description provided by Clera.
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