Research Engineer, QC Automation

About the role

An early-stage AI infrastructure company focused on reinforcement learning environments and evaluation is looking for a Research Engineer, QC Automation. You will join an engineering team and build quality-control systems for training data, helping ensure that data and evaluations remain reliable as demand grows.

Key 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.

Required profile

• 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.
• 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.

Required skills

• Proficiency with Python, Docker and Linux.
• Building scalable data-validation pipelines and automated QA/QC systems.

What we offer

• Base salary of $100,000 to $170,000 USD annually.
• Visa sponsorship available.
• On-site position in Singapore.