About this role
About the Role
Join a small, technical team working on AI evaluation and reinforcement learning infrastructure. You will design and build rigorous benchmarks for assessing AI agents on realistic, domain-specific workflows, helping research and engineering teams understand how well agents perform in practice.
What You'll Do
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Design, implement, and maintain benchmarks for evaluating AI agents on domain-specific tasks.
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Work with subject-matter experts to translate real workflows into practical tasks and evaluation criteria.
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Build reliable infrastructure to run models and agents against benchmark tasks at scale.
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Develop metrics and analyses to assess benchmark difficulty, reliability, and failure modes.
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Validate benchmark results against real-world evaluations and user needs.
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Write clear technical documentation and reports that explain methods and results.
What We're Looking For
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Two to four years of experience in software engineering, machine learning engineering, or research, including at least two years building AI benchmarks, evaluation infrastructure, or agent environments.
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Hands-on experience designing and running evaluations or benchmark environments for AI agents or large language models.
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Strong proficiency with Python, Docker, and Linux, including building and operating reliable infrastructure.
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Experience developing metrics, statistical analyses, or validation studies for evaluation systems.
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Experience collaborating with subject-matter experts and understanding workflows across technical or business domains.
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Strong technical writing, careful attention to detail, and the ability to work independently in an early-stage, fast-moving environment.
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Research publications or technical writing on benchmarking, model evaluation, or failure modes are beneficial. Experience with reinforcement learning pipelines or public benchmark projects is also useful.
Compensation & Benefits
Base salary: $100,000 to $170,000 USD annually. Visa sponsorship is available.
Location
On-site in Singapore, Singapore.