Member of Technical Staff - Research

Researcher And Research Engineer 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 perfect 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 benchmark 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, 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 evaluation needs
• Analyze model performance across benchmarks and communicate findings
• 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

• Opportunity to publish and present your work

About Us

Founding team : The core methodology behind this platform comes from NLP evaluation research we had done 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.

Tech stack : Our frontend is built in React with TSX. We use Django as our back-end framework. All of the infra is on AWS.

What We're Looking For

• Intelligence over credentials : Raw talent and research ability are more important than where you got your degree. Academic pedigree is valuable only insofar as it is a proxy for research excellence.

• Ownership : We don't have the scale or time to actively "manage" every project. Working in a small, talent-dense team, we expect everyone to show initiative to build where it's needed, not where it's asked. We strive for autonomy over consensus.

• Intensity : The LLM landscape is constantly changing. Foundation model labs are continuously pushing the frontier, creating new capabilities that demand new evaluation approaches. The companies that will emerge as leaders are being built now. Those that win will have an incredibly high speed of execution.

• Solution-oriented mindset : We're looking for researchers who see each evaluation challenge as an opportunity to design innovative solutions, not insurmountable problems.