AI Research Intern

TLDR: Davis AI is hiring an AI Research Intern to push further and scale our state-of-the-art floorplan generation model. Image-based models produce plans that break building codes under scrutiny; ours works on structure, not pixels, and is already beating all the benchmarks. You will be joining the tech team and, alongside architects, you will run falsifiable ablations on our discrete diffusion model, improve training, conditioning, and sampling at multi-GPU scale, and build the vision pipeline that turns raw plans into training data. 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 an AI Research Engineer intern 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 engineers and researchers, collaborating daily with architecture experts to turn foundational research into deployable tools. What you'll be working on: Model Architecture & Design Space exploration: Take an open question about the model and answer it properly. Read the literature, form a hypothesis, design the ablation that can actually falsify it, run it, and make sense of what you found. Model improvement: Improve our SOTA model. That means training runs on multi-GPU nodes, reading loss curves and generated samples, and iterating on architecture, loss design, conditioning, and sampling. Dataset creation: Training data is the single biggest lever on output quality. The work is building the agentic pipeline that produces it automatically, a computer vision problem at its core: parsing raster plans, PDFs and scans into a clean, consistent structured representation. What We’re Looking For Applied Research Excellence: Currently in a top MSc/PhD (or equivalent) in ML, CS, applied maths, or a closely related field. Diffusion Model Expertise: Strong understanding of diffusion models (discrete or continuous), guided generation techniques, and the latest advances in generative modeling. Technical Engineering Skills: Strong programmer in Python with experience in PyTorch. 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 Experience : Former internship in Diffusion or generative modelling 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,