About the Role
Join a small, technical team at an early-stage AI evaluation infrastructure startup building benchmarks for frontier AI agents. You will own the design and implementation of rigorous evaluations for realistic, domain-specific workflows, helping technical teams understand how agent performance translates to practical settings.
What You'll Do
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Design, implement, and maintain benchmarks for evaluating frontier AI agents on domain-specific tasks.
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Collaborate with subject-matter experts to turn real workflows into realistic benchmark tasks and evaluation criteria.
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Build reliable infrastructure to run models and agents against evaluation tasks at scale.
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Develop metrics and analyses to assess benchmark difficulty, reliability, and failure modes.
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Validate how benchmark results relate to real-world evaluations and technical user needs.
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Write clear technical documentation and reports that communicate findings to research and engineering audiences.
What We're Looking For
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Two or more years of experience in research engineering or machine learning engineering, with a focus on AI benchmarks, evaluation infrastructure, or agent environments.
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Experience designing, implementing, and operating benchmarks or evaluation environments for AI agents or large language models.
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Proficiency in Python, Docker, and Linux environments.
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Experience building infrastructure to run AI models or agents reliably, and collaborating with subject-matter experts to define tasks and criteria.
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Experience analyzing workflows across technical or business domains, and developing metrics or validation studies for benchmark quality.
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Strong technical writing, attention to detail, and ability to work independently in unstructured environments.
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Experience with reinforcement learning pipelines, data generation, published work, or public benchmarks is beneficial.
Compensation & Benefits
Annual salary range: $100,000 to $170,000 USD. Visa sponsorship is available.
Location
On-site in Singapore, Singapore.
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