Member Of Technical Staff

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.