About the role
About the Role
We are looking for exceptional engineers to join our team.
You, alongside our team, will own the platform that runs our benchmarks. This spans everything needed to evaluate LLMs at scale: Python libraries, a web platform, distributed systems, cloud infrastructure, and tooling. You'll work across the stack—whatever needs to be built to run benchmarks reliably and efficiently.
At Vals, we believe in autonomy. You will be given a high degree of independence to make decisions on tech stacks, system architecture, and code structure. You will also provide guidance to others on the team, both through informal feedback and formal processes like architecture and code reviews.
Our platform serves startups, enterprises, and research labs measuring model performance. We work with all the major foundation model labs, and some of the largest financial institutions and hospital systems in the world. 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 contribute directly to the infrastructure that makes this possible.
What You'll Do
- Build distributed systems to run evaluations across multiple models, benchmarks, and machines at scale
- Deploy cloud infrastructure using IAC, including deployment pipelines, servers, logging, monitoring, etc.
- Contribute to the internal and external libraries we maintain, including our public model library
- Develop full-stack features for our platform using React/TypeScript on the frontend and Python/Django on the backend
- Perform code and architecture reviews for other members of the team
- Help establish engineering best practices across the organization
- Collaborate closely with the research team to ensure our infrastructure meets their needs
Requirements
Technical
- Strong engineering fundamentals: You can build and ship quickly with high quality. You should have a track record of building things of significant scope (within jobs, side projects, open source, etc.)
- Python expertise: Significant experience in Python, especially in a professional setting.
- System Design Experience: You should be familiar with common concepts like VMs, containerization, load balancers, databases, etc. and when to use them appropriately.
- Familiarity with LLMs: You should have experience working with LLM APIs, and understand concepts like temperature, tokenization, reasoning, etc.
Non-technical:
- Team collaboration: Experience working with development sprints, Git workflows, and pull request reviews.
- Communication: Strong ability to provide and receive feedback effectively. This includes both spoken and written communication (e.g. design docs).
- Comfort with Ambiguity: You will often be the one taking a fuzzy problem and breaking it down into clear and actionable steps.
- Iteration speed: A tenacity to develop and iterate quickly. If you are coming from a large organization, the speed at which we ship might have a learning curve.
- Location: We are an in-person team based in San Francisco. We will support your relocation or transportation as needed.
Nice to Haves
- Experience with frontend development, ideally React.
- Experience with Django, FastAPI, or other Python-based HTTP servers
- Experience working with AWS infrastructure, including IaC
- Experience at early-stage startups or your own company
- Interest in AI/ML systems and evaluation
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
- 401K plan
- Unlimited PTO
- $1,500 housing stipend (within one-mile radius)
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.
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
- AI tools mostly fumble basic financial tasks, study finds
- The Winners (and Losers) of This New Vibe-Coding Benchmark Will Surprise You
- OpenAI’s Less-Flashy Rival Might Have a Better Business Model
- Meta Announces New AI Model in Major Test of Company’s Ambitions
- DeepSeek’s Sequel Set to Extend China’s Reach in Open-Source A.I.