AI Engineer

Why we exist

Cities still run permitting on PDFs, a rigid patchwork of systems, tribal knowledge, and long email chains. The result is bottlenecks, frustrated applicants, and not enough homes, or anything for that matter, getting built. The country is roughly 5 million homes short, and permitting delay is one of the biggest brakes on closing that gap: UCLA found that a 25% reduction in approval time increases housing production by a full 24.6%. Fewer review cycles convert almost directly into more homes.

Our mission is to make building easier, cheaper, and much faster by turning permitting into one continuous, conversation-driven workflow, guiding every step from the first zoning inquiry through final inspection. The North Star is same-day construction permits, down from the 9 to 12 months a review can take today.

What we're building

Permitting is linear but complex, spanning multiple departments and cycles of review. Our bet is that AI can turn it into one adaptive, conversational workflow that draws on both the applicant's inputs (conversation and docs) and the city's data (zoning codes, GIS, historic permits, reviewer notes) to push every step forward. That means interpreting codes, reasoning over architectural plans, managing long-running workflows across web and email, and orchestrating AI and legacy systems into accurate, traceable, cited guidance. We operate on both sides of the counter: real-time guidance for builders applying for permits, and decision support for the staff who review them.

We've mapped products directly to that process:

• PermitGuide: Answers pre-application zoning and permitting questions across web, email, and phone, in any language, always with citations, and surfaces what the community is asking.
• Application Assistant: Guides an applicant from early research through assembling a clean, staff-ready submission and into a first review that helps staff triage and clear backlog roughly 2x faster. Built on an events-based architecture that pairs deterministic checks with inference loops.
• Plan Review (next): Reads and interprets plans, automates deeper code checks, learns from reviewer comments, and keeps pushing toward a "Yes."

These are the beginning, not the destination. We're building toward an end-to-end, AI-native permitting system spanning the full lifecycle—from the first zoning question through submittal, review, inspections, code enforcement, and payments.

Just as important as the products is how we make them scale across very different jurisdictions. We're investing heavily in automating customer onboarding and in a recursive loop that turns a city's annotated historical permits into configuration. Each cycle teaches the system that jurisdiction's real rules and improves how well our configuration guides an applicant to a complete, review-ready application on the first try, which in turn produces better data to learn from.

The Role

We're building an exceptional engineering team, and the core technical problem is the reason. We're building a conversational AI system that reasons over city codes (text), architectural blueprints (visual and spatial), and GIS data (geospatial), adapting to every jurisdiction's unique rules.

This isn't a chatbot or an LLM wrapper. It's an agent-based workflow with deep memory, complex orchestration, citations, and full traceability. The work spans the stack, and every layer exists to serve that one problem.

As an AI Engineer, you'll own the reasoning layer and build across the stack around it:

• AI + Data Engineering: Build reliable, explainable services that interpret plans, codes, and emails while delivering traceable, cited outputs. Own the path from model to production.
• Core Systems & Infrastructure: Build the orchestration backbone for long-running, stateful workflows and integrations into zoning codes, GIS, and legacy permitting platforms. Elixir and the BEAM are our backbone here, though strong engineering fundamentals matter more than prior Elixir experience.
• Conversational Interfaces: Build intuitive experiences for applicants, reviewers, and city staff.
• Product Velocity: Partner across backend, AI, and frontend to ship quickly, learn from real users, and iterate with customers.
• Outsized Impact: Help define engineering standards, architecture, and culture alongside the rest of the team.

What You'll Bring

• Genuine depth in AI/ML and a passion for shipping applied AI systems into production, owning the path from model to real users.
• Strong CS and software engineering fundamentals with the ability to build reliable production services—not just research notebooks.
• Strong coding ability with experience leveraging AI coding assistants while maintaining full ownership of the output.
• Experience building agentic AI systems, including tool use, evaluations, context management, memory, and production guardrails.
• Startup experience with both greenfield development and scaling production systems.
• Deep curiosity, self-direction, and high personal standards.
• Customer-centric mindset.
• A track record of building exceptional AI-powered products or systems.
• Someone who is collaborative and enjoyable to work with.

How We Work

We're a small, high-performance team: transparent, fast, ownership-driven, and deeply focused on creating value for cities and residents. Everyone builds and ships. We work collaboratively, review each other's code, and optimize for ownership and speed.

We're heavy users of AI—particularly Claude—but humans own every review, and we genuinely value the craft of engineering. We avoid bureaucracy, minimize waste, and pursue excellence in everything we do.

Traction

We're live in multiple cities, have active pilots underway, are well-funded, and are at an exciting inflection point in our growth. We expect significant expansion over the coming year as we continue scaling our platform. Every engineer has the opportunity to shape both the architecture and the culture.

Why we exist

Cities still run permitting on PDFs, a rigid patchwork of systems, tribal knowledge, and long email chains. The result is bottlenecks, frustrated applicants, and not enough homes, or anything for that matter, getting built. The country is roughly 5 million homes short, and permitting delay is one of the biggest brakes on closing that gap: UCLA found that a 25% reduction in approval time increases housing production by a full 24.6%. Fewer review cycles convert almost directly into more homes.

Our mission is to make building easier, cheaper, and much faster by turning permitting into one continuous, conversation-driven workflow, guiding every step from the first zoning inquiry through final inspection. The North Star is same-day construction permits, down from the 9 to 12 months a review can take today.

What we're building

Permitting is linear but complex, spanning multiple departments and cycles of review. Our bet is that AI can turn it into one adaptive, conversational workflow that draws on both the applicant's inputs (conversation and docs) and the city's data (zoning codes, GIS, historic permits, reviewer notes) to push every step forward. That means interpreting codes, reasoning over architectural plans, managing long-running workflows across web and email, and orchestrating AI and legacy systems into accurate, traceable, cited guidance. We operate on both sides of the counter: real-time guidance for builders applying for permits, and decision support for the staff who review them.

We've mapped products directly to that process:

• PermitGuide: Answers pre-application zoning and permitting questions across web, email, and phone, in any language, always with citations, and surfaces what the community is asking.
• Application Assistant: Guides an applicant from early research through assembling a clean, staff-ready submission and into a first review that helps staff triage and clear backlog roughly 2x faster. Built on an events-based architecture that pairs deterministic checks with inference loops.
• Plan Review (next): Reads and interprets plans, automates deeper code checks, learns from reviewer comments, and keeps pushing toward a "Yes."

These are the beginning, not the destination. We're building toward an end-to-end, AI-native permitting system spanning the full lifecycle—from the first zoning question through submittal, review, inspections, code enforcement, and payments.

Just as important as the products is how we make them scale across very different jurisdictions. We're investing heavily in automating customer onboarding and in a recursive loop that turns a city's annotated historical permits into configuration. Each cycle teaches the system that jurisdiction's real rules and improves how well our configuration guides an applicant to a complete, review-ready application on the first try, which in turn produces better data to learn from.

The Role

We're building an exceptional engineering team, and the core technical problem is the reason. We're building a conversational AI system that reasons over city codes (text), architectural blueprints (visual and spatial), and GIS data (geospatial), adapting to every jurisdiction's unique rules.

This isn't a chatbot or an LLM wrapper. It's an agent-based workflow with deep memory, complex orchestration, citations, and full traceability. The work spans the stack, and every layer exists to serve that one problem.

As an AI Engineer, you'll own the reasoning layer and build across the stack around it:

• AI + Data Engineering: Build reliable, explainable services that interpret plans, codes, and emails while delivering traceable, cited outputs. Own the path from model to production.
• Core Systems & Infrastructure: Build the orchestration backbone for long-running, stateful workflows and integrations into zoning codes, GIS, and legacy permitting platforms. Elixir and the BEAM are our backbone here, though strong engineering fundamentals matter more than prior Elixir experience.
• Conversational Interfaces: Build intuitive experiences for applicants, reviewers, and city staff.
• Product Velocity: Partner across backend, AI, and frontend to ship quickly, learn from real users, and iterate with customers.
• Outsized Impact: Help define engineering standards, architecture, and culture alongside the rest of the team.

What You'll Bring

• Genuine depth in AI/ML and a passion for shipping applied AI systems into production, owning the path from model to real users.
• Strong CS and software engineering fundamentals with the ability to build reliable production services—not just research notebooks.
• Strong coding ability with experience leveraging AI coding assistants while maintaining full ownership of the output.
• Experience building agentic AI systems, including tool use, evaluations, context management, memory, and production guardrails.
• Startup experience with both greenfield development and scaling production systems.
• Deep curiosity, self-direction, and high personal standards.
• Customer-centric mindset.
• A track record of building exceptional AI-powered products or systems.
• Someone who is collaborative and enjoyable to work with.

How We Work

We're a small, high-performance team: transparent, fast, ownership-driven, and deeply focused on creating value for cities and residents. Everyone builds and ships. We work collaboratively, review each other's code, and optimize for ownership and speed.

We're heavy users of AI—particularly Claude—but humans own every review, and we genuinely value the craft of engineering. We avoid bureaucracy, minimize waste, and pursue excellence in everything we do.

Traction

We're live in multiple cities, have active pilots underway, are well-funded, and are at an exciting inflection point in our growth. We expect significant expansion over the coming year as we continue scaling our platform. Every engineer has the opportunity to shape both the architecture and the culture.