AI Engineer - Full time

TLDR; Davis is hiring an AI Engineer to work on the multi-agent pipeline turning raw, fragmented data into expert-grade real estate deliverables. You will make it more reliable, fast, and indistinguishable from the best human teams, then push it past what any human team could do, owning the stack end to end, from context engineering and orchestration to verification, storage and evaluation. About Davis Davis is an AI-native real estate company accelerating early-stage development and architectural design. Today developers coordinate 4-5 fragmented stakeholders over weeks or months. Soon they'll need only one: Davis. We turn every input that shapes a development decision into decision-ready outputs: investor-grade feasibility studies, investment analysis, and architect-certified designs, delivered in days. Every stage pairs our proprietary AI systems with expert review, so velocity never comes at the cost of reliability. We closed a $5.5M pre-seed co-led by Heartcore Capital and Balderton Capital , with Yellow, Evantic and Entrepreneur First, alongside angels from the founding teams of Spacemaker, Black Forest Labs, Hugging Face, Supabase, Cleo and Spore Bio. We already work with leading developers and expect to support hundreds of projects over the coming year, deepening our research, our hiring, and our coverage of the development process end to end. Where We Operate Early-stage development starts with a chain of high-stakes decisions , each requiring different data, different expertise, and different deliverables. Today, we deliver AI-powered outputs across the full spectrum - site sourcing, feasibility studies, architectural design, investment analysis, financial modeling, dataroom analysis, and more. The Role You will work on one of the core systems behind what Davis delivers. Our agents take raw, fragmented data and turn it into deliverables that real estate professionals use to make high-stakes decisions. Your job is to make that pipeline reliable, fast, and indistinguishable from work done by the best human teams - then push it beyond what any human team could do. You can expect to: Build and operate multi-agent systems that turn heterogeneous data into expert-grade deliverables across real estate development workflows. Own the system end to end : infra, orchestration, context engineering, how the system selects, structures, and injects the right information so agents behave reliably at scale. Ensure production-grade quality , performance, and reliability across every output we deliver to clients. Sit with clients and domain experts regularly to understand their constraints, challenge your own assumptions, and make sure every output meets and even exceeds clients' expectations. Beyond the technical depth, this role will expose you to how real estate decisions are made, how clients think, and what it takes to deliver outputs they trust. You'll develop a sharp business intuition alongside your engineering skills. Key Responsibilities Harness engineering: design and build the system layer around the model - context assembly, tool orchestration, verification, and report generation - to deliver consistent, high-quality outputs at scale. Data ingestion & context assembly: handle messy, unstructured project data from heterogeneous sources and ensure agents have the right context at all times. Storage & traceability: persist sources, extracted facts, intermediate results, report versions, and expert edits. Expert-in-the-loop UX: design and build the review experience (annotations, edits, approvals, diffs/version history, provenance display). Evaluation & benchmarking: build an internal eval harness (datasets, rubrics, regression tests, monitoring) to track agents performance over time. What We're Looking For 1.5+ years building and deploying production software at scale (APIs, reliability, testing, performance). Experience building and evaluating LLM agents / multi-step workflows in real systems. Proven context engineering experience: you've built systems where reliability depends on assembling the right context (RAG over heterogeneous sources, summarization, conversation state, tool outputs). Deep Python expertise (clean architecture, typing, async/concurrency, strong testing culture). Strong experience with databases + data modeling (structured storage, document storage, versioning). Full-stack experience (you can ship a real UI), with a clear backend emphasis . Comfortable building from first principles: we don't w