Founding AI Platform Engineer (Hands-On CTO Track)

Urrly•New York, NY

1d•$200,000 - $250,000•Hybrid

About The Position

Our client is a New York City-based real estate capital company working to make Homebuilder Finance a repeatable, data-driven asset class. Thousands of capable regional and local homebuilders need more efficient access to capital. Institutional investors want short-duration, collateralized residential credit exposure, but homebuilder loans remain fragmented, bespoke, inconsistently underwritten, and difficult to evaluate as a programmatic investment pool. The company is building the standard that connects them: the data model, underwriting framework, governance, workflows, and operating infrastructure that allow institutional capital to evaluate, finance, monitor, and invest in Homebuilder Finance at scale. A meaningful amount of the underlying technology and agent architecture is already operating. Real business needs—underwriting, financial models, investor materials, decisions, communications, and internal workflows—already enter the system and produce useful work. The initial bootstrap is complete. Now the company needs a second builder. You will work directly inside the codebase, extend the system, make it usable across the organization, and become the technical counterpart who can carry the product and systems vision forward without waiting for a fully specified roadmap.

Requirements

A substantial software engineering record, typically 10 or more years, including meaningful ownership of complex production systems.
Deep full-stack experience, typically five or more years, spanning modern frontend development, backend systems, data, cloud infrastructure, and production operations.
Strong experience with a modern frontend framework and JavaScript or TypeScript.
Meaningful backend and systems depth, with the ability to reason across an entire production environment.
Practical experience with deployment, observability, infrastructure, cloud systems, and DevOps.
Evidence that you have personally designed, built, tested, shipped, operated, and materially evolved consequential production systems.
Direct applied-AI experience—such as agent orchestration, LLM-powered products, AI-enabled internal tools, or serious development workflows using Claude Code, Codex, Cursor, or comparable tools.
A realistic understanding of AI’s limitations, failure modes, evaluation requirements, and appropriate role in production systems.
Experience beyond a narrow engineering lane, such as founding a company or working closely with product, operations, finance, sales, clients, or another business-facing function.
The agency to identify important gaps and act without waiting for someone to hand you a complete problem statement.
Exceptional builders with a shorter career arc are encouraged when their work demonstrates unusual technical depth, architectural judgment, zero-to-one ownership, and sustained responsibility for important production systems.

Nice To Haves

Startup or similarly high-ownership experience is strongly preferred.
Experience in lending, structured finance, capital markets, credit, real estate, or another operationally complex financial environment is valuable but not required.

Responsibilities

Extend the production platform and agent architecture across builder intake, underwriting, capital matching, reporting, communications, and internal decision-making.
Translate ambiguous, partially articulated operating problems into clear specifications and useful products.
Build a common data layer connecting builder documentation, credit analysis, investment memoranda, capital-partner requirements, and persistent builder profiles.
Design dependable workflows that combine deterministic software, AI, human judgment, approvals, and auditable override trails.
Build full-stack tools that work for ordinary business users—not only for the technical people who created them.
Own the production lifecycle from architecture and implementation through testing, deployment, observability, and iteration.
Decide what should be built, bought, automated, kept manual, or governed by human review.
Help establish the technical standards, architecture, and eventual engineering organization behind the platform.