About the role
About the Role
Join an engineering team at an AI infrastructure company focused on reinforcement learning environments and evaluation. You will build systems that make quality control for training data scalable, helping improve the reliability of data used to train and evaluate AI agents.
What You'll Do
- Automate quality control for training data generated through the platform.
- Build quality systems grounded in human judgment and understanding, rather than relying heavily on large language models.
- Define and enforce standards for training data quality.
- Design experiments and metrics to evaluate agent outputs.
- Work with data vendors to investigate quality issues, diagnose agent failure modes, and improve data generation processes.
- Turn quality findings into sampling strategies and rule-based or model-assisted validation pipelines.
- Incorporate quality insights into infrastructure 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, especially in quality control automation.
- A technical degree and proficiency with Python, Docker, and Linux.
- Experience independently building scalable data validation pipelines and end-to-end automated QA or QC systems.
- Experience with benchmarks and evaluations for reinforcement learning data, including realistic task design, reliable rubrics, and useful training trajectories.
- Ability to define and measure training data quality, and design QC systems that use human judgment without heavy reliance on large language models.
- Experience designing evaluation metrics and experiments, and collaborating with data vendors to resolve quality problems.
- Knowledge of statistics, strong communication skills, curiosity, and comfort working independently in an early-stage environment.
Compensation & Benefits
Base salary range: $100,000 to $170,000 USD annually. Visa sponsorship is available.
Location
On-site in Singapore, Singapore.