Full Stack Engineering

High-stakes industries are falling behind on AI adoption. And AI can't be trusted to give right ones because of hallucinations. Kepler is the agent harness - the infrastructure layer that wraps around AI models to make their outputs reliable, traceable, and verifiable. 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. 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. But the architecture - provenance, deterministic computation, verification - applies anywhere trust in AI output matters: chemicals, legal, healthcare. The founding team spent a combined 40+ years at Palantir building the type of large-scale data infrastructure that Kepler requires. Our CTO created Palantir's first AI platform and built the analytics engine behind $100M+ contracts. Our founding engineers led Foundry's core systems - Ontology, Fusion, Workshop, FoundryML - 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.

Full Stack Engineer

You'll build core systems that power Kepler's AI research platform. You'll work across the stack: backend services that orchestrate AI workflows, data pipelines that process billions of data points, and the infrastructure that financial professionals rely on for million-dollar decisions.

This role is for engineers who want to build foundational technology at the intersection of AI and finance, where your code directly impacts how clients make critical business decisions.

Ship the UI for a source explorer that lets users trace any value back to its exact origin in the original document

Build a new extraction pipeline for a data source we don't yet handle - with full provenance tagging and verification integrated from day one

In the longer term, you'll be given ownership of whole functional areas, from extending our platform to a new industry to leading new architecture as our infrastructure scales.

We're a close team, working together in an office in New York. We use AI tools heavily - Cursor, Claude Code, whatever makes us faster. The feedback loop on what you ship is hours, not quarters.

The pace is startup-fast but the engineering bar is high. We care about getting things right, not just getting things out. If you've worked somewhere that moves fast but ships broken software, this is different. We move as a team, not as a collection of individuals.

You've shipped production systems and you care about whether they're correct - not just whether they work on the happy path. You're comfortable in a codebase you didn't write, moving between backend services and frontend components in the same day. You're drawn to early-stage not for the title but because you want your work visible in the product, not abstracted behind three layers of management.

3-5 years building production software.

• Strong in TypeScript/React. Comfortable in backend work - our backend is Rust, but we don't require Rust experience. We believe strong engineering fundamentals and experience in other languages is what matters.
• You've built systems where correctness matters - payments, data infrastructure, healthcare, anything where a wrong output has consequences.

~ From the personal side:

You care what the analyst does with what you shipped, not whether the code was clever.

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

Backend: Rust - agent orchestration, data extraction, computation pipelines.

Frontend: TypeScript, React - the analyst workspace and verification interfaces.

Data: PostgreSQL, plus direct integrations with official data sources.

Infra: AWS.

AI: Model-agnostic by design. Learn by building production systems that power real financial research

100% covered top-of-the-line medical, dental, and vision insurance for employees and their families. Automatic coverage for life, AD&D, and disability insurance.

• Daily lunch in office.
• Unlimited PTO policy.
• Learning budget - attend any conference, course, or program that makes you better at what we're building.

We show our work, keep our promises, and flag risks before they bite. Forward-Deployed with Product DNA: We own customer outcomes while building a product company.