3 days ago
Singapore, SingaporeMid Level / Senior
Base Salary
$100k - $170k/yr
Responsibilities
- Automate quality control for training data generated in reinforcement-learning environments.
- Define and enforce data-quality standards using human judgment and evidence.
- Design experiments and metrics to evaluate agent outputs.
- Investigate quality issues with data vendors and diagnose agent failure modes.
- Build auditing systems, sampling strategies, and rule-based or model-assisted validation pipelines.
- Improve infrastructure tools and vendor portals to reduce anomalies, inconsistencies, and edge cases.
Requirements
- Two to four years of experience as a research engineer or in a similar engineering or research role focused on quality-control automation.
- Technical education or equivalent technical experience.
- Proficiency in Python, Docker, and Linux.
- Experience building scalable data-validation pipelines and end-to-end automated QA/QC systems.
- Experience with benchmarks and evaluations for reinforcement-learning training data, including realistic tasks, reliable rubrics, and useful trajectories.
- Experience defining and measuring training-data quality and designing evaluation metrics, experiments, and QA/QC processes.
- Knowledge of statistics, strong communication skills, curiosity across domains, and the ability to collaborate independently across time zones.
Benefits
- Base salary of $100,000 to $170,000 USD annually.
- Visa sponsorship is available.
- On-site work in Singapore, Singapore.