Job Opportunities API

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 co-founder 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. OUR TECHNICAL STACK - 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. We currently use Claude and GPT. The model is the replaceable part. MENTORSHIP & GROWTH You'll be directly mentored by engineers who built Palantir's core systems. Expect: - Weekly 1:1s with senior engineers who've built systems at Palantir and Meta scale. - Deep architectural reviews and guidance on system design. - Clear growth path toward technical leadership and system ownership. - Learn by building production systems that power real financial research. 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