Research Engineer, QC Automation

About the Role

Join an engineering team at an early-stage AI infrastructure company focused on reinforcement learning environments and evaluation. You will build quality-control systems for training data, helping ensure that data and evaluations are reliable as demand grows.

Compensation & Benefits

Base salary: $100,000 to $170,000 USD annually. Visa sponsorship is available.

Location

On-site in Singapore, Singapore.

Responsibilities

Automate quality control for training data produced using the company's infrastructure.; Define and enforce clear quality standards using human judgment and careful analysis, rather than relying heavily on large language models.; Design experiments and metrics to assess agent outputs and evaluation quality.; Work with data vendors to investigate quality issues, diagnose agent failure modes, and improve data-generation processes.; Turn quality-control findings into auditing systems, sampling strategies, and rule-based or model-assisted validation pipelines.; Improve infrastructure tools and vendor-facing workflows to reduce anomalies, inconsistencies, and edge cases.

Requirements

Two to four years of experience in a research engineering or similar role focused on quality-control automation, plus a degree in a technical field.; Proficiency with Python, Docker, and Linux.; Experience independently building scalable data-validation pipelines and end-to-end automated QA or QC systems.; Background in benchmarks and evaluations for reinforcement learning data, including realistic task design, reliable rubrics, and useful training trajectories.; Experience defining and measuring training-data quality, and designing experiments, metrics, and quality processes.; Strong statistical knowledge and the ability to communicate clearly across time zones and work independently in an unstructured startup environment.