Research Engineer, Benchmarks

About the Role

Join a small, highly technical team of researchers and engineers — including International Olympiad medalists and published AI researchers — at an early-stage startup building high-quality benchmarks to evaluate frontier AI agents on realistic, domain-specific workflows. As a Research Engineer, Benchmarks, you'll own the design and implementation of evaluations that frontier labs and enterprise customers rely on to measure real-world agent performance. This is a critical, high-ownership role at the intersection of research rigor and engineering execution.

The company operates in the AI/ML evaluation and reinforcement learning infrastructure space, providing a platform for building, running, and scaling RL environments and post-training datasets. The team is based in San Francisco, CA and works on-site. Visa sponsorship is available.

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 tasks for domain-specific evaluations.

• 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, customer needs, and frontier lab expectations.

• Write clear documentation and benchmark reports that make results legible and credible to technical audiences.

What We're Looking For

Required

• 2–4 years of experience in research engineering, ML engineering, or related roles — with a focus on building and delivering AI benchmarks, evaluation infrastructure, or agent environments.

• Demonstrated experience designing, implementing, and running benchmarks or evaluation environments for AI agents or large language models.

• Strong proficiency in Python, Docker, and Linux environments for building research or production infrastructure.

• Experience building and operating infrastructure to reliably run AI models or agents against benchmark or evaluation tasks at scale.

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

• Experience collaborating with subject-matter experts to translate domain workflows into benchmark tasks and evaluation criteria.

• Experience analyzing workflows across diverse technical or business domains to inform task design.

• Strong technical writing skills — able to produce benchmark reports and documentation for research and engineering audiences.

Nice to Have

• Published papers or technical blog posts on AI benchmarking, model evaluation, or model failure modes.

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

• Background at frontier AI labs, research institutions, or involvement in widely used public benchmark projects.

Traits We Value

• Deep curiosity about how workflows operate across varied domains.

• Sharp attention to detail — a habit of spotting subtle inconsistencies and edge cases in task design.

• Ability to reason from first principles about task design, scoring, and failure modes.

• Comfort thriving in unstructured problem spaces and working independently in a fast-paced, early-stage environment.

• Excellent communication skills for collaborating across time zones and with technical teams.

Compensation & Benefits

• Salary: $150,000 – $250,000 USD annually, depending on experience.

• Early-stage equity participation.

• Visa sponsorship available.

Location

This is an on-site role based in San Francisco, CA, United States. Candidates must be willing and able to work from the office. Fully remote arrangements are not available for this position.