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
Design, implement, and maintain benchmarks for evaluating frontier AI agents on domain-specific tasks.
Collaborate with subject-matter experts to turn real workflows into realistic benchmark tasks and evaluation criteria.
Build reliable infrastructure to run models and agents against evaluation tasks at scale.
Develop metrics and analyses to assess benchmark difficulty, reliability, and failure modes.
Validate how benchmark results relate to real-world evaluations and technical user needs.
Write clear technical documentation and reports that communicate findings to research and engineering audiences.
What We're Looking For
Two or more years of experience in research engineering or machine learning engineering, with a focus on AI benchmarks, evaluation infrastructure, or agent environments.
Experience designing, implementing, and operating benchmarks or evaluation environments for AI agents or large language models.
Proficiency in Python, Docker, and Linux environments.
Experience building infrastructure to run AI models or agents reliably, and collaborating with subject-matter experts to define tasks and criteria.
Experience analyzing workflows across technical or business domains, and developing metrics or validation studies for benchmark quality.
Strong technical writing, attention to detail, and ability to work independently in unstructured environments.
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.
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