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Kastle
San Francisco, California, United States
Source: Kastle careers · View original posting
From Kastle's posting. “We” and “our” refer to the employer.
Kastle is building the AI employee for consumer lending. We work with some of America's largest banks and mortgage lenders, helping them scale their contact center and compliance operations using autonomous AI Agents that process payments, originate loans, and help customers get a world class experience during the biggest financial decisions of their lives.
We've processed over $1 billion in transactions and are backed by Y Combinator and Commerce Ventures. If you are interested in building AI that is applied in the real world where precision, reliability, and performance matter and redefine access to credit across the world, apply below.
You'll be our source of truth for mortgage origination workflows and systems. Every workflow, system, and customer touchpoint from lead through closing. You'll turn deep origination expertise and the real mechanics of platforms like Encompass and Empower into the maps, documentation, and quality standards our AI is built, trained, and measured against. You'll work shoulder-to-shoulder with engineering and product to make our agents match the judgment of a top human expert on a live file. In this role you can expect to:
Map mortgage originations end to end
— every workflow across origination, processing, and closing, every customer touchpoint, and every system a lender uses (LOS, POS, PPE, AUS, credit, verifications, doc prep, e-close), at a fidelity engineers and product can build directly against.
Run quality assurance and error triage
— review live agent output, catch what's wrong, and feed structured corrections back to engineering to drive measurable quality improvements.
Build the edge-case dataset that reliable AI is built on — TRID/disclosure timing, AUS interpretation, and agency-guideline handling, especially the non-standard cases that break a deployment.
Be the domain expert the engineering, product, and deployment teams rely on — including in the room at customer go-lives.
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