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.