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.

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.