Note: This is an application for those not specializing in cybersecurity or AI safety. If you are interested in those specific domains, please see our openings for a cybersecurity researcher and an AI safety researcher. We encourage you to only apply to one research posting.

About the Role

We are looking for exceptional researchers and research engineers to design and build the next generation of AI benchmarks. You will create high-impact, challenging evaluations that push the boundaries of what we can measure in foundation models. This role is ideal for someone with deep research expertise who wants to see their work directly influence how the world evaluates AI systems.

You will lead the design and development of novel benchmarks that assess real-world capabilities of LLMs. Our benchmarks shape how foundation models are developed and generative AI applications are built. We work with every major foundation model lab - along with leading financial institutions and the application-layer companies pushing the frontier forward. Our work has been featured by the Wall Street Journal, New York Times, Washington Post, and Bloomberg.

We are building the standard for evaluating the ability of LLMs to perform real-world tasks. You will be at the forefront of defining what that standard looks like.

What You'll Do

Design and develop novel, high-impact benchmarks that assess challenging real-world capabilities

Conduct research to ensure our benchmarks are valid, reliable, and meaningful

Collaborate with foundation model labs and enterprises to understand evolving evaluation needs

Analyze model performance across benchmarks and articulate results

Publish research findings and contribute to the broader evaluation research community

Work closely with the infrastructure team to implement your benchmark designs at scale

Stay current with the latest developments in LLM capabilities and evaluation methodologies

Requirements

Advanced research experience: Master's degree or PhD in Computer Science, NLP, Machine Learning, or related field. Undergrads with very strong research backgrounds may also be considered.

Publication track record: Published papers in reputable venues (NeurIPS, ICML, ACL, EMNLP, etc.) with focus on NLP, ML evaluation, or benchmarking.

Research methodology: Strong understanding of experimental design, statistical analysis, and evaluation frameworks.

Technical skills: Proficiency in Python for research and experimentation.

Communication: Ability to clearly communicate complex research ideas to both technical and non-technical audiences.

Collaboration: Experience working in research teams and integrating feedback.

Portfolio: Demonstrated track record of impactful research work.

Location: We are an in-person team based in San Francisco. We will support your relocation or transportation as needed.

Nice to Haves

Experience specifically in LLM evaluation or benchmarking research

Familiarity with foundation model architectures and capabilities

Experience working with industry partners or in applied research settings

Background in areas like human-computer interaction, psychology, or domain-specific evaluation

Experience at early-stage startups or research labs

Contributions to open-source evaluation tools or datasets

What We Offer

Highly competitive salary and meaningful ownership. Excellence is well rewarded.

Relocation and transportation support

Health/dental insurance coverage

Lunch and dinner provided, free snacks/coffee/drinks

Unlimited PTO

$1,500 housing stipend (within one-mile radius)

Collaborate with leading AI labs and work on the front lines of AI development and evaluations

Opportunity to publish and present your work

About Us

Founding team: The core methodology behind this platform comes from NLP evaluation research we conducted at Stanford. We raised a $5M seed from some of the top institutional and angel investors in the valley. Our team has prior work experience at NVIDIA, Meta, Microsoft, Palantir and HRT. Collectively, we have over 300 citations in our published work. Our early team includes Stanford PhDs, ex-Jane Street quants, and the first designer at Snorkel.