Research Engineer, Benchmarks.

About the Role

You will own the design and implementation of rigorous, domain-specific benchmarks used to evaluate frontier AI agents on realistic workflows. Sitting within a small, highly technical team of researchers and engineers, this role is central to delivering evaluations that AI labs and enterprise customers genuinely trust and rely on.

What You'll Do

• Design, implement, and maintain high-quality 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 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 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 ML 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 building infrastructure to reliably run AI models or agents against evaluation tasks at scale.

• Experience developing metrics or validation studies to assess benchmark difficulty, reliability, and real-world correlation.

• Ability to collaborate with domain experts and translate complex workflows into evaluation criteria.

• Strong attention to detail, with a habit of spotting subtle inconsistencies and edge cases.

• Comfort working independently in fast-paced, early-stage startup environments with unstructured problem spaces.

• Excellent written communication skills for technical documentation and cross-timezone collaboration.

• Published papers or technical writing on AI benchmarking, model evaluation, or failure modes is a strong plus.

• Experience with reinforcement learning training pipelines, data generation, or RL agent evaluation is a plus.

Compensation & Benefits

Salary range: $150,000 to $250,000 USD annually. Visa sponsorship is available.

Location

On-site in Singapore.