Founding Talent

About this role

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

We think the people we hire over the next two years will matter more than any other decision we make. You'll own talent at Kepler: who we go after, how we win them, how the best engineers see us as a place to work, and whether the bar holds as we grow. You'll be the first person here whose whole job is talent, with the room to do it however works best.

Talent is also where this team comes from. Most of us joined through people we had already built with, some for a decade. That network is still our best source of great people, and we've worked only a small part of it. More recently we added outbound and a few agency partners. They help, but none of it scales to the hiring ahead: we're about a dozen people today and expect to grow several times over in the next two years, most of it engineering.

The people we want are hard to win. Most aren't looking. Many are comfortable at AI labs or big tech, and the best are weighing us against the largest offers in the industry. So we're open to doing this unconventionally. If the right move is flying out to meet someone, building a role around a person we can't pass up, bringing one of our investors into a close, or spending a year on a single relationship, you'll have the room and the budget to try it. Our CEO keeps hiring at the top of the list and will be in the conversations that matter most, and you'll decide where that time goes.

In the first few weeks you might:

- Sit down with each person on the team and turn a decade of relationships into a live map: the engineers we'd most want, why, and who knows them best.

- Pick the five people who would change Kepler most, wherever they are today, and design what it would take to win each one.

- Take over our live senior searches, starting with the candidate we're closest to losing.

- Host something the best engineers in New York would actually show up for: a technical dinner, a talk from our team on a hard problem we've solved, an evening with one of our investors.

- Rethink how we close against other strong opportunities: which conversations, which people, and which proof points move a staff engineer, and when comp comes into it.

- Tell us where our process loses great people and fix the first thing on that list.

- Decide what outbound and agency partners should do alongside the network, and keep only what earns its place.

In the longer term, you'll build the talent function around what works here: the network, the process, the employer brand, the data, and, when the volume calls for it, the team. You'll help decide which seats we open and when, and whether to create one when someone exceptional turns up. Engineering comes first, including forward deployed engineers, then go-to-market and design as those seats open.

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.

We joke that every layer of our stack is another startup's entire problem set: ingesting messy financial data, resolving it into an ontology, orchestrating agents in Rust, citing every number back to its source. That gives each engineer a large piece of the product to own, and it gives…