3 hours ago
Singapore, SingaporeMid Level
Base Salary
$150k - $250k/yr
Responsibilities
- Build and improve scalable systems that automate quality control for reinforcement-learning training data.
- Define and enforce data-quality standards using human judgment and domain understanding.
- Design experiments and metrics to evaluate agent outputs and training data.
- Investigate quality issues with data vendors, diagnose agent failures, and provide actionable feedback.
- Develop 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.
Requirements
- 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.
- 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.
- Knowledge of statistics and the ability to define data-quality standards, metrics, experiments, and quality-assurance processes.
- Strong communication skills and the ability to work independently in unstructured environments.
Benefits
- Annual salary range of USD 150,000 to USD 250,000.
- Visa sponsorship is available.
- Primary location is Singapore with an on-site work arrangement.
- Fully remote independent-contractor arrangements may be available for candidates based elsewhere.