# Frontend Software Engineer at Kepler Ai > Onsite · United States · Unspecified · Frontend · open. Source: https://alderhq.app/jobs/ashby%3Akepler-ai%3Aac08dd0a-bfda-4ef8-8a16-e33b86f7b868 (read 2026-11-10). ## Facts (observed) - Company: [Kepler Ai](https://alderhq.app/companies/kepler-ai) - Location text: New York City - Where it can be done from (inferred): Onsite · United States (evidence: "Location: New York City") - Posted: 2026-07-13 (120 days ago) - First seen by alder: 2026-11-07 · last verified: 2026-11-08 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/kepler-ai/ac08dd0a-bfda-4ef8-8a16-e33b86f7b868 - Skills listed (inferred): dbt, LLMs ## Pay and sponsorship - Posted pay: $200k–$280k per year (from the listing's pay field) - Market band for similar posted roles (New York, n 19): p25 $148k, median $170k, p75 $198k ## Description (the employer's text) ### 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 You'll build the analyst-facing surface of Kepler's AI research platform: the product financial professionals rely on for million-dollar decisions. The platform produces cited, verifiable data; you'll package it into the workflows analysts run every day (researching a company, pulling comps, extracting financials into a model) and build the verification interfaces that let them trace any number back to its source. 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. In the first few weeks you might: Ship the UI for a source explorer that lets users trace any value back to its exact origin in the original document. Build the interface that shows an agent's work in real time: which sources it's reading, what it's computing, and where each value came from. Make our financial statement views feel like a native spreadsheet: fast rendering of thousands of cells, keyboard navigation, every cell one click from its source. Rework how long-running research queries stream into the workspace, so analysts see cited results as they arrive instead of waiting on a spinner. 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. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Akepler-ai%3Aac08dd0a-bfda-4ef8-8a16-e33b86f7b868 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.