Lead AI / ML Staff Engineer

Position Overview

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.

Key Responsibilities

• Create training datasets from imperfect sources (LiDAR scans, scraped construction docs).
• Combine 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.

Required Qualifications

• 4+ Years of experience training and deploying real-world AI models. Deep understanding of model architectures, training pipelines, and evaluation methodologies.
• Creativity and technical depth to explore, evaluate, and iterate on different algorithmic approaches.
• Ability to turn unstructured or incomplete data into usable training sets and rapidly prototype models under real constraints.
• Strong background in algorithms and data structures with experience designing performant solutions to spatial and generative challenges.
• 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., type hints, MyPy, or Pydantic).