Member of Technical Staff
Vals AI
Member of Technical Staff
Vals AI
Independent AI model evaluation platform
Note: This is an application for those specifically interested in building benchmarks to assess cyber capabilities. If you are interested in other domains, please see our general research posting. We encourage you to only apply to one research posting.
We are looking for an exceptional researcher to design and build the next generation of AI benchmarks for cybersecurity. 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 and risks 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.
Design and develop novel, high-impact benchmarks that assess challenging real-world capabilities for cybersecurity
Conduct research to ensure our benchmarks are valid, reliable, and meaningful
Collaborate with foundation model labs and enterprise security teams to understand evolving evaluation needs
Analyze model performance across benchmarks and articulate results
Publish research findings and contribute to the broader security and evaluation research communities
Work closely with the infrastructure team to implement your benchmark designs at scale
Stay current with the latest developments in LLM cyber capabilities and evaluation methodologies
Cybersecurity experience: Hands-on work in security, e.g., as a security engineer, penetration tester, red teamer or vulnerability researcher. You may have found and reported vulnerabilities through bug bounty programs or CVEs.
Systems depth: Strong grounding in operating systems, cloud infrastructure and networking.
Interest in benchmarking: You care about measuring capability rigorously, not just demonstrating it. Ideally, you've built cyber evals, CTF challenges or security benchmarks before.
AI-forward: You already use LLMs in your security work and want to work with them every day.
Communication: Ability to clearly communicate complex research ideas to both technical and non-technical audiences.
Collaboration: Experience working in research teams and integrating feedback.
Location: We are an in-person team in San Francisco and will support relocation or transportation as needed.
You've previously worked as a security engineer at a security company or on an in-house security team, and/or you have experience on an AI or ML team at a security company
Strong ties to the security community: CTF teams, conference talks, open-source security tools, or research collaborations, published security research, papers or well-read technical write-ups
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
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.
We recently announced our $40M Series A at a $400M valuation, led by Andreessen Horowitz, with participation from existing investors 8VC, Pear VC, and Bloomberg and new investors Hudson River Trading and NextLadder Ventures.
Tech stack: We use Python for most things at Vals AI. Our platform is built on Django, with a React frontend. All of the infra is on AWS using CDK for IaC.
What We're Looking For
Learning velocity: The role encompasses a wide variety of tasks. Rather than expecting you to be an expert on Day 1, we are looking for someone who can learn new skills and technologies quickly.
Ownership: 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. The unicorn companies that will emerge from this technology shift are being built now. Those that win will have an incredibly high speed of execution.
Solution-oriented mindset: We're looking for people who see opportunities to craft solutions at each juncture, not those who pass hard problems to others or admit defeat.
Vals in the Media:
Vals AI provides independent evaluation and benchmarking for large language models and AI applications with domain-specific leaderboards. It tests models on real-world tasks using private datasets to prevent test leakage and reports metrics such as tool use, multi-modality, long-context reasoning, accuracy, latency, and cost, while offering an evaluation framework for private labs and enterprise teams and public benchmarks like Finance Agent and Valkyrie. The company differentiates itself by delivering unbiased, practitioner-focused measurements built with domain experts and by focusing on economically valuable, complex professional tasks rather than academic problems. Its goal is to become the trusted measurement layer for the AI economy, helping research labs and enterprises make informed decisions about AI model adoption and deployment.
Explore more opportunities at Vals AI