Research Engineer, Benchmarks

About the Role This is a core technical role on a small, high-caliber team building rigorous 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 the credibility and impact of the company's benchmark platform. What You'll Do - Design, implement, and maintain 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 well-scoped evaluation tasks. - Build 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 and customer 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 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. - Hands-on experience designing and running benchmarks or evaluation environments for AI agents or large language models. - Experience developing metrics and validation studies to assess benchmark difficulty, reliability, and real-world correlation. - Experience collaborating with domain experts to turn workflows into concrete evaluation criteria. - Strong technical writing skills; published papers or blog posts on AI benchmarking, model evaluation, or failure modes are a plus. - Experience with reinforcement learning training pipelines, data generation, or RL agent evaluation is a plus. - Background at a frontier AI lab, research institution, or on a widely used public benchmark project is a plus. - Comfort working independently in fast-paced, early-stage environments with unstructured problem spaces. - Sharp attention to detail and the ability to reason from first principles about task design, scoring, and edge cases. Compensation & Benefits Salary range: $150,000 to $250,000 USD annually. Visa sponsorship is available. Location On-site in Singapore .