Member of Technical Staff - Research

Vals AI•San Francisco, CA

2d•Onsite

About The Position

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.

Requirements

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
Strong understanding of experimental design, statistical analysis, and evaluation frameworks
Proficiency in Python for research and experimentation
Ability to clearly communicate complex research ideas to both technical and non-technical audiences
Experience working in research teams and integrating feedback
Demonstrated track record of impactful research work

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

Responsibilities

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