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San Francisco, California, United States
Hybrid
Location: San Francisco Bay Area or New York City, with frequent travel as needed.
Format: In-office 4 days a week with flexibility for the occasional remote days
Businesses make consequential decisions with an incomplete view of the world.
What to invest in. Which customers to pursue. Where a market is moving. What risk to take. What to do next.
Their own information is scattered across systems, spreadsheets, conversations, and people’s heads. Meanwhile, public filings, market activity, company announcements, and other outside signals may reveal that something important has changed. People spend enormous amounts of time piecing this together, but the reasoning behind the eventual decision often remains invisible.
Fricative is building toward a Decision Management System: a way to bring those signals together and help a team answer a practical question: What should we do, why, and why now? The answers have to be more than persuasive. People should be able to see where each claim came from, how the evidence fits together, what remains uncertain, and what would change the recommendation. We’re developing this with real clients and real decisions because this is a category that doesn’t have a playbook yet.
Fricative is a portfolio company of super{set}, a venture studio that conceives, funds and builds AI-native companies from formation through scale. We work alongside founders as operators, helping shape companies from day one.
There isn't an established AI Solutions playbook to follow. You'll help write it while doing the work. The best way to build this system is to stay close to the decisions our customers are actually trying to make. You'll get close to a client's business, learn which decisions matter, how they make them today, and what they can't currently see. You'll draw out what different people know, notice where their accounts conflict, and earn enough trust to get past the polished version of how things work.
You'll investigate relevant public sources, find signals worth paying attention to, and connect them with what the client knows internally. You'll use AI to gather, test, and synthesize evidence at a scale that would be difficult to manage manually.
You'll turn the work into something a system can actually do. That means breaking a messy problem into the right building blocks, deciding what each skill should and should not do, and stitching those skills into a workflow that produces a useful answer. You'll test the workflow against real examples, find where skills fail, overlap or contradict each other, and tighten the system until it works reliably. When something requires an integration or capability the system doesn't have, you'll know enough to surface the gap and work with engineers to close it.
Then you'll turn that work into something useful: a clear, sourced account of what is happening, a defensible recommendation, and a way for the team to act at the right time. You'll explain the reasoning to the people who will make, challenge, and carry out the decision. You'll test whether they understand and trust it, learn where the story or the system breaks down, and improve both.
Some days you'll be learning a new industry. Others, you'll be chasing a surprising signal back to its source. Another day, you may be building a prototype or sitting with a client to understand why a recommendation didn't lead to action. You are the person who can figure out what the room should be building and not the one who writes the most code in the room.
You'll help turn real customer problems into solutions and, ultimately, into a new kind of Decision Management System:
In the first 6–9 months, you will have:
This is an unusual combination of strengths. The center of gravity isn't any one discipline.
We need someone who can connect those modes of thinking: moving between customer, evidence, technology, and action while keeping sight of the decision. Someone who can explore divergently, then impose useful structure.
A non-linear background that doesn't fit neatly into one discipline. Maybe you've worked in consulting, research, journalism, analytics, operations, product, or a technical customer-facing role. Maybe you've built something on your own.
What matters more than the label is the pattern: you found a problem, investigated it, formed a view, built or tested something, changed your mind when the evidence changed, and made the result useful to someone else. You've probably experienced one or more of these:
We want to see examples where you: Found the real problem. Investigated it. Formed a view. Built or recommended something. Tested it. Changed your opinion. Made the result useful to someone else.
If you've ever found yourself looking at a messy problem and thinking, “There has to be a better way to figure this out.” Let's talk.
