Research Engineer / Scientist (3D Reconstruction)

What You Will Do

• Design and implement modern 3D reconstruction systems, including feed-forward and optimization-based approaches for geometry, appearance, and scene understanding.
• Research, prototype, and productionize advanced 3D representations (e.g., implicit functions, point-based or volumetric methods, hybrid representations) with a focus on accuracy, efficiency, and scalability.
• Develop and improve optimization pipelines for multi-view reconstruction, including camera pose estimation, joint geometry/appearance optimization, and robust loss formulations.
• Build end-to-end training and evaluation workflows for 3D reconstruction models, from data preparation and supervision strategies to large-scale experiments and metrics.
• Collaborate with data and infrastructure teams to ensure reconstruction methods integrate cleanly with existing 3D data pipelines, rendering systems, and downstream applications.
• Analyze failure modes and data quality issues in real-world reconstruction scenarios, and design principled solutions to improve robustness and generalization.
• Optimize performance across the stack, including memory usage, training speed, and inference latency, to support large-scale datasets and production constraints.
• Contribute to technical direction by proposing new research ideas, mentoring teammates, and helping set best practices for 3D reconstruction across the organization.

Key Qualifications

• 6+ years of experience working on 3D reconstruction, multi-view geometry, or related areas in computer vision, graphics, or machine learning.
• Strong foundation in modern 3D reconstruction techniques, including feed-forward neural methods or optimization-based approaches.
• Deep experience with 3D representations and their tradeoffs (e.g., implicit fields, point-based methods, meshes, volumes) or with large-scale optimization pipelines for reconstruction.
• Proficiency in Python and/or C++, with hands-on experience building research or production systems.
• Experience with deep learning frameworks (e.g., PyTorch) and numerical optimization tools.
• Familiarity with rendering, differentiable rendering, or graphics pipelines, and how they interact with reconstruction systems.