About this role
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
- 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.
Location
On-site in Singapore, Singapore.