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.
What You'll Do
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Automate quality control for training data produced using the company's infrastructure.
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Define and enforce clear quality standards using human judgment and careful analysis, rather than relying heavily on large language models.
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Design experiments and metrics to assess agent outputs and evaluation quality.
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Work with data vendors to investigate quality issues, diagnose agent failure modes, and improve data-generation processes.
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Turn quality-control findings into auditing systems, sampling strategies, and rule-based or model-assisted validation pipelines.
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Improve infrastructure tools and vendor-facing workflows to reduce anomalies, inconsistencies, and edge cases.
What We're Looking For
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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.
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Proficiency with Python, Docker, and Linux.
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Experience independently building scalable data-validation pipelines and end-to-end automated QA or QC systems.
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Background in benchmarks and evaluations for reinforcement learning data, including realistic task design, reliable rubrics, and useful training trajectories.
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Experience defining and measuring training-data quality, and designing experiments, metrics, and quality processes.
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Strong statistical knowledge and the ability to communicate clearly across time zones and work independently in an unstructured startup environment.
Compensation & Benefits
Base salary: $100,000 to $170,000 USD annually. Visa sponsorship is available.
Location
On-site in Singapore, Singapore.
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