We're building the missing data layer in financial markets: physical risk.
Physical risk — drought, heatwaves, flooding, wildfire — impacts more than half of global GDP and costs companies hundreds of billions of dollars per year.
But markets can't price it. The data that exists is vague: climate "scores" and 2050 scenarios no investor can actually rely on.
We're already working with 3 of the world's top 10 asset managers (over $30 trillion AUM) to solve this problem.
Kepler turns events in the physical world into a number investors trust — asset-level, dollar-denominated, point-in-time Earnings-at-Risk, built to sit next to a Bloomberg feed on an investor's desk.
The engine is already live, but we need to build from a few thousand assets to tens of millions.
Closing that gap, and turning our working system into the category-defining global risk platform, is the central engineering challenge of the company.
Our 10-year vision: Kepler is one of the most important companies in finance, thanks to a world model that can accurately predict how events in the physical world will impact assets, companies, and markets.
That's what this role will tackle. You'll be our senior founding engineer — one of the first few engineers, and the one who helps define how we get where we need to go.
What you'll ownThe platform — the pipeline from raw satellite, climate, and disclosure data to a number institutions trust, and the work of scaling it from hundreds of assets to the whole market.
Production rigor — what "correct" means here: reproducible, point-in-time, monitored. The difference between a research result and a dataset people bet capital on.
Delivery — the APIs, data shares, and reports that put our numbers directly into how investors work.
The engineering bar — as the first senior hire, you help set the culture, the quality standard, and the build-vs-buy calls the team lives with.
Exactly how each of these looks is still being written. You'll have a real hand in writing it.
What we're looking for4–8 years building data-heavy products in production.
Willingness to dig in with our customers and understand their pains, forward-deployed engineer style. You don't need previous customer experience, just an openness to talking to customers.
Genuinely at home in large, messy datasets — not just competent, but excited by making them trustworthy at scale. This is the one non-negotiable.
A full-stack generalist who reaches for whatever the problem needs. We care far less about which languages you've lived in than how you think — rigidity about a specific stack is, if anything, a yellow flag.
High agency: you find the problem and fix it without being asked, and move from architecture to implementation in the same afternoon.
You ship working systems over perfect prototypes.
Someone we'll want to build alongside for thousands of hours.
Some exposure to finance — an internship, a stint at a bank / fund / PE / credit shop, quant work, or just a real interest in how markets work.
Any exposure to risk modeling, financial or adjacent (e.g. insurance).
Geospatial / remote-sensing experience.
We'd rather hire someone very smart and very curious who can grow into the domain than someone who ticks every box.
$150K–$200K base + 0.5–1.5% founding-level equity.
Health-insurance reimbursement (QSEHRA), 2 weeks PTO to start — and a real commitment to grow benefits at every raise and milestone.
An awesome office space to work from in San Francisco
The scope to define the product and the engineering org, on work that lands on the desks of the largest institutions in the world.
Curiosity. Pragmatism. Transparency.
ProcessIntro call with CEO · 15 min — mutual fit
Intro call with CTO · 30 min — your technical background
Deep dive with the founders · 60 min — more about your background
Working Session + Success Plan — 120 min (2 parts) — present your work on an assigned problem; discussion re: how you can succeed in the role
References
1-Day Working Interview — assessing long-term fit together
Offer
Fill in the form here and upload your CV. Questions? Email [email protected]
Skills Required
- 4-8 years building data-heavy products in production
- Comfort and excitement working with large, messy datasets and making them trustworthy at scale
- Full-stack generalist able to work across architecture and implementation
- Willingness to engage directly with customers and understand their needs
- High agency: find problems and deliver solutions from architecture to implementation
- Focus on shipping robust, production-ready systems rather than perfect prototypes
- Exposure to finance (internship, bank/fund/PE/quant work) or strong interest in markets
- Exposure to risk modeling or insurance-adjacent risk experience
- Geospatial / remote-sensing experience
What We Do
Kepler Labs develops transparent, AI-powered physical-risk intelligence for institutional investors. Its platform identifies facilities, maps revenue exposure, and models climate and nature hazards—including heat, flooding, wildfire, wind, and water stress—at asset level. The company converts complex physical-world events into defensible, dollar-denominated financial metrics such as earnings-at-risk, helping asset managers evaluate resilience, conduct diligence, and incorporate climate risk into investment decisions.
