Overview
About the Role This is a top-priority hire 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 companies using the platform, building systems that scale quality to meet growing demand. The team is small, technical, and fast-moving, with backgrounds spanning competitive programming, AI research, and startup founding. What You'll Do Automate QC for training data created 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 training data across the platform. Design experiments and metrics to evaluate and grade agent outputs. Partner with data vendors to debug quality issues, diagnose agent f
Full job description
Full Job Description
About the Role
This is a top-priority hire 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 companies using the platform, building systems that scale quality to meet growing demand. The team is small, technical, and fast-moving, with backgrounds spanning competitive programming, AI research, and startup founding.
What You'll Do
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Automate QC for training data created by companies using the platform's infrastructure.
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Build QC systems grounded in true understanding and human judgment, rather than heavy reliance on LLMs.
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Define and enforce quality standards for training data across the platform.
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Design experiments and metrics to evaluate and grade agent outputs.
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Partner with data vendors to debug quality issues, diagnose agent failure modes, and improve their data generation processes.
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Translate QC learnings into auditing systems, including sampling strategies and rule-based or model-assisted validation pipelines.
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Continuously integrate QC insights into infrastructure tools and vendor-facing systems to reduce anomalies, inconsistencies, and edge cases.
What We're Looking For
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2 to 4 years of experience in engineering or research roles, ideally with a focus on QC automation or data quality.
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Proficiency in Python, Docker, and Linux environments.
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Proven experience building scalable data validation pipelines and automated QA/QC systems end-to-end, without a fully prescribed roadmap.
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Experience working on benchmarks and evaluations for RL training data, including defining realistic tasks, reliable rubrics, and useful trajectories.
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Experience creating QC systems based on human judgment rather than LLM-centric approaches.
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Experience designing experiments and metrics to grade agent outputs, and partnering with data vendors to provide actionable feedback.
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Solid knowledge of statistics and comfort designing metrics and QA/QC processes.
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Strong written and verbal communication skills for working across time zones.
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Genuine curiosity, comfort in unstructured problem spaces, and the ability to work independently in a fast-paced startup environment.
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 fully remote independent contractors (particularly from Europe) may also be considered depending on location.
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Verification notes
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