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You will automate quality control systems for reinforcement learning training data and define quality standards to ensure high-performance model outputs. Additionally, you will partner with data vendors to diagnose failure modes and integrate findings back into the infrastructure to improve data generation processes.
This is the top-priority hire on the engineering team at a fast-growing AI infrastructure startup focused on reinforcement learning environments and post-training data. As a Research Engineer, QC Automation, you will own the automation of quality control for training data created by companies using the platform, building systems that scale quality to meet strong and continued demand. You will join a roughly 15-person engineering group of published researchers and experienced AI practitioners.
Automate QC for training data produced by companies using the platform's infrastructure.
Build QC systems grounded in true understanding and human judgment, 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 their data generation processes.
Translate QC findings into auditing systems, including sampling strategies and rule-based or model-assisted validation pipelines.
Continuously feed QC learnings back into infrastructure tools and the data vendor portal to reduce anomalies, inconsistencies, and edge cases.
2 to 4 years of experience in an engineering or research role focused on QC automation or a closely related area.
Proficiency in Python, Docker, and Linux environments.
Proven track record 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 defining and measuring quality standards for training data and designing experiments to grade agent outputs.
Hands-on experience partnering with data vendors to debug quality issues and provide actionable feedback.
Solid knowledge of statistics and comfort designing metrics and QA/QC processes.
Strong written and verbal communication skills for cross-timezone collaboration.
Comfort working autonomously in unstructured, fast-paced, early-stage startup environments.
Salary range: $150,000 to $250,000 USD annually. Visa sponsorship is available.
On-site in Singapore. Fully remote independent contractor arrangements may also be considered for candidates based outside Singapore, particularly in Europe.
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