About the Role
Join a small technical research and engineering team building benchmarks for evaluating AI agents on realistic, domain-specific workflows. You will own the design and implementation of rigorous evaluations that help technical teams understand how agents perform in real-world tasks.
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 benchmark tasks and evaluation criteria.
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Build and operate reliable infrastructure for running models and agents against tasks at scale.
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Develop metrics and analyses to assess benchmark difficulty, reliability, and failure modes.
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Validate whether benchmark results align with real-world performance and technical user needs.
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Write clear documentation and reports for research and engineering audiences.
What We're Looking For
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Two to four years of experience in research engineering, machine learning engineering, or a related technical role, including at least two years building AI benchmarks, evaluation infrastructure, or agent environments.
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Strong Python skills and practical experience with Docker and Linux.
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Experience designing, implementing, and running benchmarks or evaluation environments for AI agents or large language models.
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Experience collaborating with subject-matter experts and analyzing workflows across technical or business domains.
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Experience developing metrics, statistical analyses, or validation studies for evaluations.
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Strong technical writing, attention to detail, and ability to work independently in an early-stage environment.
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A technical educational background; experience with reinforcement learning pipelines, published evaluation work, or widely used benchmarks is a plus.
Compensation & Benefits
Salary range: USD 150,000 to 250,000 annually. Visa sponsorship is available.
Location
On-site in Singapore, Singapore.
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