AI Researcher - Full time at Meetdavis

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.

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.

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.

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.

• 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.

• Collaboration & Communication: Strong teamwork skills. Able to articulate complex concepts clearly and work closely with AI engineers, researchers, and domain experts to integrate technical solutions into the architectural design workflow.

• Industry Experience: Background in a top AI research organization or cutting-edge startup, especially working on foundation models or generative AI tools.

• Publications/Open Source: Research publications in generative modeling (diffusion, VAEs, flows, GANs) or significant open-source contributions demonstrating ability to push state-of-the-art systems.

• Domain Knowledge: While not required, an interest in architecture or design will help.

• Reinforcement Learning & Optimization: Experience with RL, reward modeling, or constrained optimization (particularly MCTS, GRPO and RLHF) relevant to guiding generative models under complex constraints.

• Multimodal Generation: Experience with graph-based, sequence-based, or discrete structured generative models (e.g., molecule generation, layout generation, program synthesis) or with graph neural networks.

You're joining a team of 12 at the very beginning - where every decision you make shapes the product, the culture, and the trajectory of the company. What you build here will be yours.

• Build what no one has before: the foundation model that automates architectural design and redefines how cities are imagined, designed, and built.

• Work on meaningful challenges: from constraint-aware generative models to real-world deployment in major construction projects.

• Competitive salary and meaningful equity in an early-stage company.

• Ship fast, iterate boldly: go from research to prototype to production in weeks, not years.

• Join a world-class team: a mix of AI researchers, engineers, and architects backed by world-class VCs.

Team

Mehdi (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 (CTO) is an AI researcher from École Polytechnique who worked extensively on discrete diffusion and turned down a PhD with Google DeepMind to build Davis. They started working together in July 2025 at Entrepreneur First's first European residency, a two-month lock-in in a German castle.

Today we're a team of 12: technical profiles from Polytechnique, ENS and INRIA alongside architects and deep real estate expertise.

We're small with an extremely high bar. If you want to work deeply on hard problems and see your work reach clients within days, you're the one we need.

Why We'll Win

Real estate is a $13 trillion industry that technology has largely bypassed. The professional services that feed it (design, engineering, feasibility, permitting) represent hundreds of billions in spend that no one has seriously automated.

Proptech spent the last decade selling SaaS on the edges of these workflows. It didn't work, for two reasons: no professional wants another tool to learn, and no tool can automate work that runs on expert judgment. Davis makes a different bet. We don't sell tools, we sell the work: AI-generated, expert-validated, delivered in days instead of weeks. Every project compounds our data advantage across typologies, geographies and regulatory contexts.

Why No One Has Solved Architectural Design Yet

Real estate development bleeds time and money in architectural design loops. Architects cycle through dozens of floorplan revisions to meet regulatory and client constraints, each round taking days, each missed constraint restarting the loop. Traditional CAD and BIM tools offer zero generative capability; parametric tools only check constraints after generation, leading to designs that frequently break under new zoning rules or irregular sites.

Generative AI has the potential to solve this, but doesn't yet. Fine-tuning image diffusion models on floorplans produces layouts that look plausible but fall apart under scrutiny: hallucinated rooms, mislabeled spaces, code violations no architect would accept. Pixel-space models have no concept of what a wall is or why a corridor needs to connect two things. Compliance-guidance techniques typically require segmentation at each noisy timestep, compounding errors and making major edits impractical. As a result, floorplans may “look” plausible but break building codes, or need heavy post-processing before they're usable.

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.

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