Research Engineer / Scientist (3d Reconstruction)

World Labs World Labs · AI Frontier · San Francisco, CA · 3DGM

World Labs is seeking a Research Engineer/Scientist specializing in 3D Reconstruction to develop and advance state-of-the-art methods for creating high-quality 3D geometry and appearance from real-world data. This role involves working with modern reconstruction techniques, novel representations, robust optimization, and scalable training/inference pipelines at the intersection of computer vision, graphics, and machine learning. The goal is to translate cutting-edge research into production-ready systems for spatial intelligence.

What you'd actually do

  1. Design and implement modern 3D reconstruction systems, including feed-forward and optimization-based approaches for geometry, appearance, and scene understanding.
  2. 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.
  3. Develop and improve optimization pipelines for multi-view reconstruction, including camera pose estimation, joint geometry/appearance optimization, and robust loss formulations.
  4. Build end-to-end training and evaluation workflows for 3D reconstruction models, from data preparation and supervision strategies to large-scale experiments and metrics.
  5. Collaborate with data and infrastructure teams to ensure reconstruction methods integrate cleanly with existing 3D data pipelines, rendering systems, and downstream applications.

Skills

Required

  • Python
  • C++
  • PyTorch
  • numerical optimization tools
  • deep learning frameworks
  • 3D reconstruction
  • multi-view geometry
  • computer vision
  • graphics
  • machine learning
  • 3D representations
  • optimization pipelines
  • rendering
  • differentiable rendering
  • graphics pipelines

Nice to have

  • feed-forward neural methods
  • implicit fields
  • point-based methods
  • meshes
  • volumes
  • large-scale optimization pipelines

What the JD emphasized

  • 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.

Other signals

  • 3D reconstruction
  • spatial intelligence
  • world models
  • computer vision
  • graphics
  • machine learning