Research Engineer, Benchmarks

ABOUT THE ROLE This role sits at the heart of a small, technical team building high-quality benchmarks to evaluate frontier AI agents on realistic, domain-specific workflows. You will own the design and implementation of evaluations that frontier labs and enterprise customers rely on to understand real-world agent performance. The work is critical to ensuring benchmarks are rigorous, credible, and practically meaningful. WHAT YOU'LL DO - Design, implement, and own the quality of internal benchmarks for evaluating frontier agents on domain-specific tasks. - Partner with subject-matter experts to define realistic workflows and translate them into evaluation criteria. - Build reliable infrastructure to run models and agents against benchmark tasks at scale using Python, Docker, and Linux environments. - Develop metrics and statistical analyses to measure benchmark difficulty, reliability, and failure modes. - Validate that benchmark performance correlates with real-world evaluations and customer needs. - Write clear technical documentation and benchmark reports for research and engineering audiences. WHAT WE'RE LOOKING FOR - 2 to 4 years of experience in research engineering or machine learning engineering, with a focus on AI benchmarks, evaluation infrastructure, or agent environments. - Strong proficiency in Python, Docker, and Linux for building research or production infrastructure. - Demonstrated experience designing and running benchmarks or evaluation environments for AI agents or large language models. - Experience developing metrics, statistical analyses, or validation studies to assess benchmark quality and real-world correlation. - Experience collaborating with domain experts to translate workflows into structured evaluation tasks. - Strong technical writing skills, with published papers or technical posts on AI benchmarking, model evaluation, or failure modes being a plus. - Ability to reason from first principles about task design, scoring, and edge cases. - Comfort working independently in fast-paced, early-stage startup environments with unstructured problem spaces. - Experience with reinforcement learning training pipelines, data generation, or RL agent evaluation is a bonus. COMPENSATION & BENEFITS Salary range: $150,000 to $250,000 USD annually. Visa sponsorship is available. LOCATION On-site in Singapore. This is a full-time, in-person role.