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
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Build and improve quality control systems for training data created within reinforcement learning environments.
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Define quality standards and design experiments and metrics to assess agent outputs.
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Develop auditing approaches, including sampling strategies and rule-based or model-assisted validation pipelines.
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Work with data vendors to diagnose quality issues, understand agent failure modes, and improve data generation processes.
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Turn quality control findings into improvements to internal infrastructure and vendor-facing tools.
What We're Looking For
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Two to four years of experience in a research engineering or similar role, including hands-on work in quality control automation.
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Proficiency with Python, Docker, and Linux, plus experience building scalable data validation and automated QA/QC systems end to end.
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Experience with benchmarks and evaluations for reinforcement learning training data, including realistic tasks, reliable rubrics, and useful training trajectories.
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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.
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Knowledge of statistics and experience designing metrics, experiments, and quality assurance processes.
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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.
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