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Research Scientist developing robust 3D reconstruction systems using multi-view geometry, SfM, SLAM, bundle adjustment, deep multi-view stereo, learned matching, and monocular depth. Responsibilities include designing pose estimators and reconstructors, scaling methods to real-world datasets, evaluating accuracy and quality, integrating state-of-the-art models, and collaborating on production deployment. Requires advanced computer vision research expertise, Python and PyTorch proficiency, and practical experience with COLMAP, ORB-SLAM, and Ceres.

The role involves building and maintaining ML and cloud infrastructure for training large-scale generative models. Responsibilities include designing GPU clusters, supporting distributed training, managing storage systems, and collaborating with researchers and engineers.

The role involves building and operating ML systems and infrastructure for large foundation models, data pipelines, and experiment orchestration in a collaborative environment.

Lead research on designing, building, training, and optimizing diffusion-based generative models for high-quality 3D content from diverse inputs, emphasizing world-scale scenes.