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.