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About the Role
Work on the multi-agent pipeline turning raw, fragmented data into expert-grade real estate deliverables. Make the pipeline 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.
Responsibilities
- 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 delivered to clients.
- Sit with clients and domain experts regularly to understand their constraints, challenge assumptions, and ensure every output meets and exceeds client expectations.
- 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.
Requirements
- 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: 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 (can ship a real UI), with a clear backend emphasis.
- Comfortable building from first principles: prefer a lightweight, well-engineered codebase over heavy agent frameworks.
Skills
- Python
- Databases
- Data modeling
- Full-stack development
- Backend development
- Context engineering
- LLM agents
- Multi-step workflows
- RAG
- Summarization
- Conversation state
- Tool outputs
- Clean architecture
- Typing
- Async/concurrency
- Testing
- Observability
- Quality monitoring
- GIS
- Geocoding
- NLP
Location
- Remote
Work Type
- Full-time
Experience Level
- 1.5+ years
Benefits
- Competitive salary
- Meaningful equity
About the Company
- Davis is an AI-native real estate company accelerating early-stage development and architectural design.
- 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 proprietary AI systems with expert review, ensuring velocity never comes at the cost of reliability.
- Closed a $5.5M pre-seed co-led by Heartcore Capital and Balderton Capital, with Yellow, Evantic and Entrepreneur First, alongside angels from founding teams of Spacemaker, Black Forest Labs, Hugging Face, Supabase, Cleo and Spore Bio.
- Real estate is a $13 trillion industry that technology has largely bypassed.
- Proptech spent the last decade selling SaaS on the edges of these workflows; Davis sells the work: AI-generated, expert-validated, delivered in days instead of weeks.
- Every project compounds our data advantage across typologies, geographies and regulatory contexts.
Equal Opportunity
- We care about who you are, not just what's on your CV.
- If you're drawn to what we're building but don't meet every requirement, we still want to hear from you.
- Studies show that women in particular tend to apply only when they meet 100% of the criteria. If that's you, please don't let that hold you back. We'd love to receive your application.