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
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Design, implement, and own the quality of internal benchmarks for evaluating frontier agents on domain-specific tasks.
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Partner with subject-matter experts to define realistic workflows and tasks for domain-specific evaluations.
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Build reliable infrastructure to run models and agents against benchmark tasks at scale.
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Develop metrics and statistical analyses that measure benchmark difficulty, reliability, and failure modes.
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Validate that benchmark performance correlates with real-world evaluations, customer needs, and frontier lab expectations.
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Write clear documentation and benchmark reports that make results legible and credible to technical audiences.
What We're Looking For
Required
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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.
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Demonstrated experience designing, implementing, and running benchmarks or evaluation environments for AI agents or large language models.
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Strong proficiency in Python, Docker, and Linux environments for building research or production infrastructure.
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Experience building and operating infrastructure to reliably run AI models or agents against benchmark or evaluation tasks at scale.
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Experience developing metrics, statistical analyses, or validation studies to assess benchmark difficulty, reliability, and real-world correlation.
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Experience collaborating with subject-matter experts to translate domain workflows into benchmark tasks and evaluation criteria.
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Experience analyzing workflows across diverse technical or business domains to inform task design.
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Strong technical writing skills — able to produce benchmark reports and documentation for research and engineering audiences.
Nice to Have
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Published papers or technical blog posts on AI benchmarking, model evaluation, or model failure modes.
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Experience with reinforcement learning training pipelines, data generation, or RL agent evaluation.
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Background at frontier AI labs, research institutions, or involvement in widely used public benchmark projects.
Traits We Value
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Deep curiosity about how workflows operate across varied domains.
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Sharp attention to detail — a habit of spotting subtle inconsistencies and edge cases in task design.
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Ability to reason from first principles about task design, scoring, and failure modes.
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Comfort thriving in unstructured problem spaces and working independently in a fast-paced, early-stage environment.
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Excellent communication skills for collaborating across time zones and with technical teams.
Compensation & Benefits
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Salary: $150,000 – $250,000 USD annually, depending on experience.
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Early-stage equity participation.
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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.
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