We are looking for a Lead AI & Algorithms Engineer to own the full lifecycle of novel spatial model development; defining the right framing of complex inference problems, structuring image, video, and vectorized floorplan data into usable training formats, training and evaluating models, and deploying performant systems into our production stack. You’ll be responsible for developing and advancing our diffusion models and 3D generative models that power Dream’s core spatial intelligence.
Upcoming Challenges
Create training datasets from imperfect sources (LiDAR Scans, scraped construction docs)
Combining heuristics, labeling tools, and clever preprocessing (e.g. object detection models) to generate high-quality data at scale.
Design and train novel spatial deep learning models that intelligently infer room layouts, surface boundaries, and object placement from partial inputs — balancing architectural plausibility with flexibility and precision.
Explore hybrid approaches that combine classical geometry algorithms with learned models.
Benchmark and iterate on model performance across multiple axes — accuracy, speed, generalization
Integrate AI models directly into the product stack, working closely with full-stack engineers to ensure outputs are usable in the interactive 3D design environment.
What we Require
4+ Years of experience training and deploying real-world AI . Deep understanding of model architectures, training pipelines, and evaluation methodologies.
Creativity and technical depth to explore, evaluate, and iterate on different algorithmic approaches.
Scrappiness and speed in turning unstructured or incomplete data into usable training sets, and in getting models built and tested under real constraints.
Strong background in algorithms and data structures, with the ability to design performant solutions to spatial and generative problems.
Hands-on experience with procedural generation techniques, especially in the context of 3D geometry, modeling, or simulation.
Expert proficiency in Python, with a strong preference for experience using static typing (e.g., with type hints, MyPy, or Pydantic).