About the Role:
We’re hiring a Technical Content Lead to shape how Vals explains some of the most important questions in AI: which models actually work, how enterprises should evaluate them, and how the industry should measure progress as capabilities improve.
You’ll write across three parts of Vals:
Vals Smith, our product for evaluating models and agents on the actual work companies do. Our public benchmarks and research, where we independently measure frontier models across increasingly complex domains. Emerging policy and standards conversation around AI evaluation, where rigorous measurement is becoming increasingly important to how labs, enterprises, and policymakers understand model capabilities. You’ll work closely with our research, engineering, product, and customer teams to turn technical work into sharp, accessible writing: benchmark reports, product launches, customer stories, model analyses, technical explainers, and occasional policy pieces.
We want someone who can understand the underlying work and develop a distinctive point of view on what matters. The goal is to make Vals one of the most trusted voices on how AI is actually performing — in benchmarks, inside companies, and as the technology becomes important enough to require better standards around how we measure it.
What You’ll Do:
Interview customers, researchers, engineers, and technical leaders and turn those conversations into strong long-form pieces.
Help establish Vals Smith as the leading way for enterprises to evaluate models and agents on their own workflows through product launches, customer stories, technical case studies, and original analysis.
Ghostwrite and edit for Vals leadership and technical team members when their perspective should be part of the conversation.
Help decide what Vals should be writing about in the first place — not just execute against a content calendar.
What We're Looking For:
A portfolio of long-form technical writing — published essays, research blog posts, lab posts, or industry reporting.
Strong technical literacy. You don't need to be able to train a model, but you need to be able to read a paper, sit in on a research review, and come out with something accurate and sharp.
An editorial point of view. You should have opinions about what's interesting, push back on weak takes, and not just transcribe what people say.
Speed. We ship content the day a major model releases. You should be able to turn a publishable post in hours, not weeks.
Ability to work in-person, in San Francisco. We will support your relocation as needed.
Nice to Haves:- Prior research communications experience at an AI lab or research-led startup
- A technical background (CS, ML, math, sciences) that you've since translated into writing — e.g., engineer or PM turned writer.
- Prior writing for a top-tier technical publication or engineering blog
What we offer:- Highly competitive salary and meaningful ownership. Excellence is well rewarded.
- Relocation and transportation support
- Full health, dental, and vision 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 had done at Stanford. We work with all the major foundation model labs, some of the largest financial institutions, and hospital systems in the world. 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.
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 YouOpenAI’s Less-Flashy Rival Might Have a Better Business ModelMeta 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.
