Introducing Kepler

The Problem

High-stakes industries are falling behind on AI adoption. Their workflows can’t afford wrong answers. And AI can’t be trusted to give right ones because of hallucinations. The barrier isn’t that the models aren’t smart enough. It’s that no one can verify what they produce. The fix isn’t a better model, it’s a trust layer: every output traceable, every calculation auditable, every answer reproducible.

What Kepler Is

Kepler is the agent harness - the infrastructure layer that wraps around AI models to make their outputs reliable, traceable, and verifiable. The model is a replaceable component. The harness is the product.

In Kepler's architecture, the LLM orchestrates - it decides what data to gather, what to compute, how to structure the output. But every actual data point, every extracted value, every calculation flows through deterministic code pipelines. The LLM never touches the data itself. Every value carries provenance metadata back to its exact source. Every computation is auditable and reproducible. Verification loops cross-check outputs before users ever see them.

We started in finance because the stakes are highest and the tolerance for error is zero. We’ve built a finance research product that lets analysts supercharge their workflow: pulling comparables, building models and researching filings. No more double-checking every number AI spits out. Every number tracing back to the source, every time.

But the architecture - provenance, deterministic computation, verification - applies anywhere trust in AI output matters: chemicals, legal, healthcare. Models are commoditizing fast. The trust layer is what's missing and the market is massive.

The Team

The founding team spent a combined 40+ years at Palantir building the type of large-scale data infrastructure that Kepler requires. Our founding engineers led Foundry's core systems - Ontology, Fusion, Workshop, FoundryML, created Palantir's first AI platform and scaled data products at Meta to 1B+ users.

We’ve paired this deep technical foundation with a repeat founder profile. Our CEO built and scaled a data company to $15M ARR before successfully selling it. He then became Citadel's first Head of Business Engineering, experiencing first hand the problems we are now solving. We have a team who’ve been on both sides: building systems like this at massive scale and selling it into the buyers who need it most.

We’re backed by investors who built the modern AI and data stacks, plus the builders of iconic commercial businesses. This includes founders of OpenAI, Meta AI Research, MotherDuck, dbt Labs and Square as well as PebbleBed, Company Ventures and Mantis VC firms.

The Role

What You'll Own

You'll own how we get in the room with the investment teams we want to work with: who we meet, where we meet them, and how a first conversation turns into a working relationship. The people we work with are portfolio managers, CIOs, CTOs, and heads of data and AI at hedge funds, private equity firms, and allocators. Most of them don't answer cold email. The conversations that matter start with an introduction, a dinner, or ten minutes at a conference, and they go somewhere when the person across the table feels understood.

A lot of the job is teaching. Most firms are still working out what AI should do inside their walls, and many of their engineers are trying to build it themselves. You'll explain how our system works, why we split the work between code and the model, how it fits the data and tools a firm already has, and what deploying on their data actually involves. You'll often do that for a PM and a CTO in the same meeting, so you'll need to understand the technical side in real depth.

This is a senior individual contributor role with no quota or territory. You'll be the person a PM treats as a peer: someone who has sat in a seat like theirs, talks about their workflow in their language, and leaves a meeting with a sharper read on what they need than they gave. Our CEO stays close to our most important relationships. Your job is to make sure there are more of them, at the firms that matter most to us.

Some of this you'll do alongside other members of the team, but all of it is yours to elevate and make stronger.

• First conversations. Discovery with new firms: how a team works today, where its data lives, what its engineers are already building, and what a first engagement would need to prove. You'll run these yourself and bring in our CEO and engineers when the conversation calls for it.

• Conferences and events. Which ones we go to, who we meet before we arrive, and what happens after. Also the rooms we host ourselves: dinners and roundtables small enough that the right twelve people actually show up.

• Shaping each deal. No two of our deals look alike. One firm wants our API under the agents its engineers are building, another wants analysts working in our app, another wants one workflow running on its own data before anything else. You'll work out which one you're looking at, and shape the pitch and the offer around it with our CEO and product team.

• The buyer's voice in product and marketing. What PMs, CTOs, and heads of data tell you, turned into clear input for our product team and into how we describe Kepler: the story for each segment, the decks, the one-pagers. You'll use what we build the way a user would, and say so when it misses.

• Keeping it all moving. Inbound requests, live conversations, follow-ups, NDAs, scoping sessions, and materials, across dozens of relationships at once and over months. You'll own the coordination, so every conversation has a clear next step and nothing goes quiet.

• Relationships and partnerships. The people in your network and ours who should know Kepler, and the path to each of them: customers, investors, advisors, and the people they'd introduce us to. Also the firms that sit next to our buyers every day, like prime brokers, fund administrators, and data providers, that could bring us into conversations we can't reach on our own.

• Market read. Which firms are adopting AI and how, what competitors are telling the same buyers, and which segments we should go after next.

In the first few weeks you might:

• Learn our system well enough to walk a CTO through it, from where their data lives to how every number in an answer gets cited.

• Take over our inbound and live conversations, and make sure each one has a clear next step.

• Map the people you and the team already know at the firms we want to work with, and pick the twenty conversations worth having first.

• Choose the next conference worth our time, set up the meetings before we arrive, and follow up on every one.

• Host a dinner for a dozen people who run research or data at investment firms, and turn a few of those seats into second conversations.

• Sit in on a live deal and tell our product team the three things the buyer would push back on, and how you'd change the pitch.

In the longer term, you'll build how we grow our relationships with investment firms: the events, the partner channels, the advisors, how we run a deal from first meeting to signature, and, when the volume calls for it, the team. You're the first person here whose whole job is the market, so a lot of what the function becomes is going to be your call.

How We Work

We're a close team, working together in an office in New York (with Wagyu, our office dog). We use AI tools heavily: Claude, Cursor, whatever makes us faster. Fluency is assumed. Our engineers work directly with each customer on the customer's own data, and you'll often be in those rooms with them.

The pace is startup-fast but the bar is high. Our buyers have heard every pitch for AI in finance, and they remember the ones that didn't hold up. Our sales cycles are long: a deal often takes months of conversations, a scoping session, a security review, and an evaluation phase, with a PM, a CTO, and often someone above them weighing in before anyone signs. We'd rather have fewer conversations that turn into real work than a calendar full of meetings that go nowhere.

The team has strong backgrounds and low ego. We expect everyone to roll up their sleeves and handle the unglamorous problems: the follow-up note at 11pm after a conference dinner, the 7am breakfast before a client meeting, the meeting list that has to be rebuilt the day before an event. We move as a team, not as a collection of individuals.

Who You Are

You've worked on the buy side, or close enough to it that portfolio managers and CIOs treat you as one of their own. People take your calls because you've been useful to them for years. You'd rather be in the room than behind a screen, and you can tell a meeting that went well from a relationship that's going somewhere. You want to understand how things actually work, and you're happy to spend an afternoon with an engineer to get there. You're drawn to early stage because you want to build those relationships from the start, with a product you can stand behind.

From the craft side:

• 5+ years at a hedge fund, private equity firm, or allocator, or in a seat that works with them every day: capital introduction, corporate access, research, or advisory. No upper limit, comp scales with experience.

• A current network at the firms we work with. Come ready to tell us who you'd call first and why they'd pick up.

• You've built relationships that turned into business: capital raised, clients won, a deal that happened because you made the introduction.

• You can run a first conversation with a PM, a CTO, or a head of data, learn how their team actually works, and leave with a clear next step.

• You know how investment teams work and how the technology underneath them works. You can talk about a model or a filing with a PM, and about data pipelines, APIs, and security with a CTO. You'll learn our system well enough to explain it and show it yourself, and you'll know when to bring in an engineer.

• You can hold a long deal together. You keep dozens of relationships and live conversations moving over months, and nobody has to ask you where one stands.

• You're a natural host: picking the room and the guest list, and making a dinner of near-strangers worth everyone's evening.

• You don't need a sales background. What matters is that buyers trust you and want to keep talking to you.

• AI fluency is part of the job. You'll use Claude to prepare for meetings, research firms, and keep your relationships organized. If that sounds implausible to you, this isn't the right fit.

From the personal side:

• You measure your work by the relationships that turn into customers, not by the meetings on your calendar.

• You'd rather send the follow-up the same day than the perfect note next week.

• You'll tell anyone here, our CEO included, when a pitch, a target, or an event isn't working, before it costs us a relationship.

• You communicate before it's a problem, and when a teammate needs something from you, they don't have to ask twice.

• You know what it feels like when the plan changes twice in a day and the work still has to ship.

Don't check every box? Apply anyway. We prioritize judgment about people, credibility with buyers, technical depth, and drive over a specific résumé shape.

Mentorship & Growth

• You'll work most closely day-to-day with strategy & ops, product and our CEO. You’ll have a front row view into how a business is built.

• Time with our engineers on every deal. You'll learn the system from the people who built it.

• Real budget and the authority to spend it: conferences, dinners, travel, and the events worth hosting.

• Growth here means scope first, then team. You're our first hire focused on the market, and what the function becomes is mostly what you make it.

Working at Kepler

Our Benefits

• 100% covered top-of-the-line medical, dental, and vision insurance for employees and their families. HSA maxed by the company to the IRS limit.

• Automatic coverage for life, AD&D, and disability insurance.

• Daily lunch in office.

• Unlimited PTO policy.

• Development environment budget - latest MacBook Pro, multiple monitors, ergonomic setup, and any development tools you need.

• "Build anything" budget - dedicated funding for whatever tools, libraries, datasets, or infrastructure you need to solve technical challenges, no questions asked.

• Learning budget - attend any conference, course, or program that makes you better at what we're building.

Our Operating Principles

• Trust as the Default: People do their best work when confidence is mutual. We show our work, keep our promises, and flag risks before they bite. Trust isn't an aspiration - it's the baseline.

• Forward-Deployed with Product DNA: We own customer outcomes while building a product company. We don't win if they don't win.

• Extreme Ownership: If you notice a problem, you own it by making sure it doesn’t fall through the cracks. Authority comes from initiative, not job titles. Once you step up, you're accountable for the outcome.

• Production-First Engineering: We design for critical workloads from day one. Durable execution, blue/green deploys, automated rollbacks, continuous delivery with end-to-end observability.

• Communicate with Intent: Great work disappears without great communication. We push information to the people who need it, when they need it. Silence is never the safe choice.

• Earn it Every Day: Your work speaks for itself. We create an environment where the best idea wins, the strongest work gets recognized, and everyone is held to the same high standard.

• Keep Raising the Bar: Great teams compound. Every hire raises the bar, every win gets named, every person gets the tools and runway to grow.

Kepler is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind. We are committed to the principle of equal employment opportunity for all employees and to providing employees with a work environment free of discrimination and harassment.