Research Engineer, QC Automation
About the Role
This is a top-priority role on the Engineering team at an early-stage AI infrastructure company focused on reinforcement learning environments and post-training data. You will own the automation of quality control for training data produced by external data vendors, building systems that scale quality assurance as demand grows. The team is a small, high-calibre group of researchers and engineers working at the frontier of AI alignment.
What You'll Do
Automate QC for training data created by companies using the platform's infrastructure.
Build QC systems grounded in human judgment and true understanding, rather than heavy reliance on LLMs.
Define and enforce quality standards for RL training data.
Design experiments and metrics to grade agent outputs.
Partner with data vendors to debug quality issues, diagnose agent failure modes, and improve data generation processes.
Translate QC learnings into auditing systems, including sampling strategies and rule-based or model-assisted validation pipelines.
Continuously integrate QC learnings into infrastructure tools and the vendor portal to reduce anomalies, inconsistencies, and edge cases.
What We're Looking For
2 to 4 years of experience in research engineering or a similar role focused on QC automation.
Proficiency in Python, Docker, and Linux environments.
Proven experience building scalable data validation pipelines and automated QA/QC systems end-to-end, without a fully prescribed roadmap.
Experience working on benchmarks and evaluations for RL training data, including defining realistic tasks, reliable rubrics, and useful trajectories.
Experience creating QC systems based on human judgment rather than LLM-heavy approaches.
Experience designing experiments and metrics to grade agent outputs.
Experience partnering with data vendors to debug quality issues and provide actionable feedback.
Strong knowledge of statistics and comfort designing metrics and QA/QC processes.
Strong written and verbal communication skills for collaborating across time zones.
Ability to work autonomously and thrive in unstructured, fast-paced early-stage environments.
Compensation & Benefits
Salary range: $150,000 to $250,000 USD annually. Visa sponsorship is available.
Location
On-site in Singapore. Candidates based in San Francisco or working remotely as an independent contractor, particularly from Europe, may also be considered.