Jobs at Dream Technologies

Build end-to-end core product features including interactive 3D browser rendering, generative layout algorithms, backend APIs and data flows. Integrate production ML models, shape the data model and infrastructure, collaborate with AI engineers, and iterate with pilot customers while influencing engineering culture and tooling.

Lead development of spatial deep learning models (diffusion and 3D generative) from data curation through training, evaluation, and production deployment; create scalable training datasets from LiDAR, images, videos, and floorplans; combine geometry algorithms with learned models and integrate outputs into an interactive 3D product stack.

Develop, train, evaluate, and deploy spatial generative AI models for home renovation and design. Define inference framing, prepare image-based datasets with labeling partners, iterate with user feedback, and deploy performant models into production architecture.

Lead architecture, build production-grade code, set technical direction, establish engineering culture, own infrastructure and CI/CD, mentor and grow the team, and coordinate cross-stack delivery to scale product performance and velocity.

Hands-on role building and scaling 3D web engines, converting LiDAR/camera data into accurate 3D models, extruding floorplans, placing objects, and translating internal 3D formats to/from BIM (e.g., Revit). Work includes rendering engine evaluation (WebGL, Unreal, Unity) and asset pipeline development.