AI Research Engineer

AI Research Engineer – Discrete Diffusion Models for Architectural Design

About Davis

Davis is setting a new time standard for real estate development. We're targeting the $650B pre-construction market , where real estate developers today must coordinate with 4–5 fragmented stakeholders over weeks or months. In the future, they'll only need one: Davis.

We integrate every input that shapes a development decision into decision-ready outputs - investor-grade feasibility studies, investment analysis, and architect-certified designs delivered in days. Davis combines proprietary AI systems with expert review at every stage, ensuring velocity without compromising reliability.

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 Research Engineer to spearhead this effort in our Paris office. If you’re excited about pushing the state-of-the-art in generative models 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 architecture experts to turn foundational research into deployable tools.

What you’ll own:

• Model Architecture & Design Space : Design the core model architecture and define 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 against current state-of-the-art approaches in layout and floorplan generation. You will 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 : Strong programmer 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.

Nice to Have

• 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.

Why Join Us

You're joining a team of 6 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.