About the Role
Join a small, technical team at an early-stage AI evaluation company building benchmarks for frontier AI agents. You will own the design and implementation of rigorous, domain-specific evaluations that help research teams and customers understand real-world agent performance.
What You'll Do
Design, implement, and maintain benchmarks for evaluating AI agents on domain-specific tasks.
Work with subject-matter experts to translate real workflows into realistic tasks and evaluation criteria.
Build reliable infrastructure for running models and agents against benchmark 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 user needs.
Write clear technical documentation and reports communicating benchmark methods and results.
What We're Looking For
Two to four years of experience in research engineering, machine learning engineering, or a related technical role, including at least two years building or delivering AI benchmarks, evaluation infrastructure, or agent environments.
Strong proficiency in Python, Docker, and Linux for research or production infrastructure.
Hands-on experience designing, implementing, and operating evaluations for AI agents or large language models.
Experience building infrastructure to run models or agents reliably, and developing metrics, statistical analyses, or validation studies.
Ability to collaborate with subject-matter experts and analyze workflows across different domains to create useful evaluation tasks.
Strong technical writing, attention to detail, first-principles reasoning, and comfort working independently in a fast-paced startup.
Published research or technical writing on benchmarking, model evaluation, or failure modes is valued. Reinforcement learning pipelines, data generation, and agent evaluation experience are also relevant.
Compensation & Benefits
Base salary: $100,000 to $170,000 USD annually. Visa sponsorship is available.
Location
On-site in Singapore, Singapore.