TLDR; Davis AI is looking for an AI Engineer Intern to help solve one of the hardest problems in applied AI: building multi-agent systems that remain reliable across long, complex real-estate workflows. You will work on the core delivery pipeline - from context assembly and tool orchestration to verification, storage, evaluation, and observability.

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 - feasibility studies, architectural design, investment analysis, financial modeling, and more.

You will:

Own one delivery pipeline end to end: market, financial, or regulatory analysis.
Build the harness around the model: context engineering, tool orchestration, verification, report generation.
Build the tools agents rely on: open data (cadastre, zoning, transactions, demographics), extraction from regulatory PDFs and plans etc.
Turn every expert correction into source memory, gold datasets and regression evals.
Extend the eval harness: LLM judges, replay caches, traces, prompt optimization.
Ship production-grade quality and reliability on every output a client receives.
Sit with clients and domain experts, challenge your assumptions, and learn how real estate decisions are actually made.

You'll fit right in if you:

Write strong backend Python: clean & scalable architecture, typing, async, real tests.
Build thin, well-tested harness layers around LLM programs rather than stacking frameworks.
Read and write French: our sources and deliverables are in French.
Can sit with an architect and work out what "correct" means.

A big plus:

Built or evaluated LLM agents in real systems; (DSPy or GEPA for ex).
Operated long-running, cost-controlled LLM pipelines: caching, replay, retries, spend tracking, observability.
GIS: shapely, projections, parcels and zoning layers, open data.
Computational geometry or constrained optimization.
Document understanding: PDF parsing, OCR, layout detection, plan vectorization.
Databases and data modeling with versioning.
Multi-country products: heterogeneous sources, localization, varying rules.
Interest in real estate, architecture or urban planning.

Why Davis

Build what no one has before: the system that delivers feasibility, investment analysis and design end to end
Accelerate your craft: learn from an exceptional team while taking real ownership from day one
Meaningful challenges: reliable agents over messy real-world data, deployed on major construction projects
Competitive salary
Ship fast: prototype to production in weeks, not years
A world-class team of AI researchers, engineers and architects, backed by world-class VCs

More information about Davis, the team and the market we’re going after:

Team

Mehdi (Co-founder & CEO) grew up in a family of architects and has lived this problem firsthand. He's a repeat founder who bootstrapped his first startup at 20, and graduated from Sciences Po and HEC Paris. Amine (Co-founder & CTO) is an AI researcher from École Polytechnique who w

AI Engineer Intern at Meetdavis