Research Engineer, Benchmarks

ABOUT THE ROLE This is a hands-on research engineering role focused on designing and owning high-quality benchmarks that evaluate frontier AI agents on realistic, domain-specific workflows. You will sit within a small, highly technical team and play a critical part in ensuring evaluations are rigorous, credible, and trusted by leading AI labs and customers. 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 benchmark tasks and evaluation criteria. - Build and operate reliable infrastructure to run models and agents against benchmark tasks at scale. - Develop metrics and statistical analyses that measure benchmark difficulty, reliability, and failure modes. - Validate that benchmark performance correlates with real-world evaluations, customer needs, and frontier lab expectations. - Write clear technical documentation and benchmark reports for research and engineering audiences. WHAT WE'RE LOOKING FOR - 2 to 4 years of experience in software engineering, ML engineering, or research roles, with a focused track record in AI benchmarks or evaluation infrastructure. - Strong proficiency in Python, Docker, and Linux environments. - Demonstrated experience designing, implementing, and running benchmarks or evaluation environments for AI agents or large language models. - Experience building infrastructure to reliably run AI models or agents against benchmark or evaluation tasks. - Ability to analyze and model workflows across diverse technical or business domains to support task design. - Sharp attention to detail with a habit of spotting subtle inconsistencies and edge cases. - Comfort reasoning from first principles about task design, scoring, and failure modes. - Strong written communication skills; experience producing technical documentation or benchmark reports. - Ability to thrive in unstructured problem spaces at an early-stage startup. - Bonus: experience with reinforcement learning pipelines, data generation, or RL agent evaluation; published work on AI benchmarking or model evaluation. COMPENSATION & BENEFITS Salary range: USD 150,000 to 250,000 annually. Visa sponsorship is available. LOCATION On-site in Singapore.