Kepler
Hiring now
Founding Talent
On-site · New York City, New York, United States$170k–230kFull-timePosted 5 weeks ago
Kepler
ApplyKepler
Hiring now
On-site · New York City, New York, United States$170k–230kFull-timePosted 5 weeks ago
Kepler
ApplyHigh-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.
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 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.
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.
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 you a pitch that holds up when a skeptical staff engineer starts asking questions.
Our bar is senior and specific. We hire engineers who can take a big, unstructured problem and own it end to end without anyone managing them, and we'd rather keep a seat open than fill it with someone who doesn't raise the team. Talent sits at the center of how the company runs. You'll be in every debrief, your read on a candidate will be critical, and you'll be expected to tell anyone here, our CEO included, when a profile, a loop, or an offer is wrong.
The team has strong backgrounds and low ego. We expect everyone to roll up their sleeves and handle the unglamorous problems: the onsite that has to be rebuilt across four calendars, the reference call at 8pm, the offer letter that needs to go out tonight. We move as a team, not as a collection of individuals.
You've won people who weren't looking, for companies they hadn't heard of, and they were glad they said yes. Sourcing is a craft for you and closing is the part of the job you love. You could run talent at a company many times our size, and right now you still want to be the one finding and winning people yourself.
From the craft side:
5+ years in recruiting, most of it technical, including real time in-house at a fast-growing startup with a high bar. No upper limit, comp scales with experience.
You've personally sourced and closed senior engineers against offers from AI labs and big tech. Come ready to walk us through the hardest one.
You've run a campaign to win a single person: months of relationship-building, the right people in the right conversations, and an offer shaped around what they cared about.
You build relationships on a long arc. Some of the people you meet this year will join in two.
You read an engineering resume the way an engineer does. You can tell someone who owned a system from someone who worked near one, and you know which employers train people well.
You've built recruiting from scratch before, or joined early enough to help build it: the pipeline, the interview loop, the scorecards, the reporting.
You run your funnel on data. You know where candidates drop, which sources convert, and how long each stage takes, and you act on it.
AI fluency is part of the job. You'll build your own agents and automations with Claude and Ashby instead of waiting for an engineer to do it. If that sounds implausible to you, this isn't the right fit.
From the personal side:
You measure your work by what your hires go on to build.
You'd rather try the unconventional idea and tell us how it went than wait for permission.
You'll tell anyone here that a pitch or an offer is off before the candidate walks, not after.
You communicate before it's a problem, and when a teammate or a candidate 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 taste, range, and drive over a specific résumé shape.
You'll work most closely day-to-day with strategy & ops and our CEO. You’ll have a front row view into how a business is built.
Real budget and the authority to spend it: events, travel, tools, and the unconventional ideas worth trying.
Growth here means scope first, then team. You're the first talent hire, and what the talent function becomes is mostly what you make it.
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.
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.