Mid-Level Research Engineer, QC Automation

About the Role Join the engineering and research team at an early-stage AI evaluation infrastructure startup, building quality control systems for training data used in reinforcement learning. This role is central to scaling reliable data quality through human judgment, clear standards, and practical automation. What You'll Do Build and improve scalable systems that automate quality control for training data. Define and enforce data quality standards using human judgment and domain understanding, without relying heavily on large language models. Design experiments and metrics to evaluate agent outputs and training data. Work with data vendors to investigate quality issues, diagnose agent failures, and provide actionable feedback. Turn quality findings into sampling strategies, audits, and rule-based or model-assisted validation pipelines. Integrate quality checks into internal tools and vendor workflows to reduce anomalies and edge cases. What We're Looking For Two to four years of experience in research engineering or a related technical role focused on 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 data, including realistic tasks, reliable rubrics, and useful training trajectories. Ability to define and measure data quality standards, and to design metrics, experiments, and quality assurance processes. Knowledge of statistics, strong communication skills, and the ability to work independently in unstructured environments. Compensation & Benefits Annual salary range: USD 150,000 to USD 250,000. Visa sponsorship is available. Location The primary location is Singapore, with an on-site work arrangement. Fully remote independent contractor arrangements may be available for candidates based elsewhere.