17 hours ago
Singapore, SingaporeMid Level
Base Salary
$100k - $170k/yr
Responsibilities
- Build automated quality control systems for training data using human judgment and data analysis.
- Define and enforce quality standards for reinforcement learning training data.
- Design experiments and metrics to assess agent outputs and training data quality.
- Investigate data quality issues with vendors, diagnose agent failure modes, and improve data-generation processes.
- Develop sampling strategies and rule-based or model-assisted validation pipelines.
- Improve infrastructure tools and vendor workflows to reduce anomalies, inconsistencies, and edge cases.
Requirements
- Two to four years of experience in research engineering or a similar quality-control automation role.
- Proficiency with Python, Docker, and Linux.
- Experience building scalable data validation pipelines and end-to-end automated QA or QC systems.
- Background in reinforcement learning data benchmarks and evaluations, including task design, reliable rubrics, and useful training trajectories.
- Experience measuring training data quality, designing experiments and metrics, and creating quality-control processes.
- Strong statistical understanding, communication skills, curiosity, and ability to work independently in unstructured, fast-paced environments.
Benefits
- Annual salary of $100,000 to $170,000 USD.
- Visa sponsorship is available.
- On-site work in Singapore, Singapore.