TLDR; Davis is hiring an AI Researcher to push the frontier of floorplan generation. Working directly with architects, you'll own research directions end-to-end, from diffusion and flow matching models (continuous and discrete) to deployment in a product used by real estate professionals.

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.

Our Mission

We build a foundation model for architectural design that generates compliant, editable building layouts from scratch. By leveraging discrete diffusion models (operating on structured representations rather than pixels), we aim to produce floorplans and site plans that respect real-world constraints (zoning laws, space requirements, etc.) and can be iteratively refined like a human-designed plan.

The Role

We are looking for a AI Researcher to lead this effort in our Paris office. If you’re excited about improving our current state-of-the-art floorplan generative model and applying it to a high-impact domain, this role offers a unique opportunity to define a new class of AI-driven design tools. You will work within a focused team of 3–4 engineers and researchers, collaborating daily with architects to turn foundational research into deployable tools.

What you'll own:

Model Architecture & Design Space: Design the core model architecture and work on a discrete design space for architectural layouts. You will choose how to represent floorplans (e.g. as graphs of rooms/connections or token grids) such that the diffusion model’s outputs are editable and code-compliant by construction. This involves ensuring the model can enforce architectural rules (e.g. room sizes, adjacency constraints) within its generation process.
Large-Scale Model Training: Lead the training of a foundation diffusion model from scratch on GPU clusters. You’ll set up distributed training across multiple nodes, optimize data loading and checkpointing, and manage experiments at scale. The role requires hands-on engineering for efficient training of large models on high-performance computing infrastructure.
Benchmarking & Iteration: Evaluate the model’s performance and establish benchmarks to measure success. Using these evaluations, you’ll iterate on the model to push performance beyond existing methods. Our goal is to surpass the latest research results and produce genuinely useful architectural designs.

What We’re Looking For

Applied Research Excellence: PhD or Master’s in Maths, Computer Science, Machine Learning, or a related field, or equivalent experience. Strong foundations in ML and a track record of innovative research, publications, or high-impact projects.
Diffusion Model Expertise: Deep understanding of diffusion models (discrete or continuous), guided generation techniques, and the latest advances in generative modeling.
Large-Scale Model Training: Proven experience training large-scale deep learning models on GPU clusters. Comfortable with distributed training, multi-node jobs, experiment management, and handling large datasets.
Technical Engineering Skills: Proficiency in Python with experience in PyTorch or similar frameworks. Ability to write efficient, maintainable code and optimize training pipelines. Familiarity with distributed training libraries (e.g., PyTorch Lightning) is a plus.
Research Literacy: Ability to read, evaluate, and implement advanced ML research. You stay current with state-of-the-art generative modeling work and can adapt cutting-edge methods to domain-specific problems.

AI Researcher - Full time at Meetdavis