Research Software Engineer, Computer Vision

Google Google · Big Tech · Zürich, Switzerland

Research Software Engineer focused on computer vision and ML for XR applications, specifically 3D reconstruction, scene understanding, and novel view synthesis. The role involves research, development, and optimization of 3D scene representation pipelines, bridging the gap from research to product.

What you'd actually do

  1. Develop innovative computer vision and machine learning technology focused on 3D reconstruction, pose estimation, and novel view synthesis for XR applications.
  2. Drive the research, development, and optimization of 3D scene representation pipelines (such as Gaussian Splatting and Neural Representations), including improving model efficiency, training stability, and visual fidelity for large-scale scenes.
  3. Establish and maintain relationships with main stakeholders and keep recurring updates on the advancements of the project, making sure that their expectations are aligned to the research and engineering work.

Skills

Required

  • software development
  • Computer Vision
  • ML infrastructure
  • AI conferences

Nice to have

  • Master's degree or PhD
  • industry or academic research lab experience
  • generative AI techniques
  • image diffusion models
  • 3D computer vision
  • deep learning
  • Gaussian Splatting (3DGS)
  • Neural Radiance Fields (NeRF)
  • 3D scene reconstruction

What the JD emphasized

  • publication record in AI conferences

Other signals

  • develops novel core technology around computer vision and ML
  • focus on scene and object understanding and 3D computer vision
  • work across the full range from research to product
  • focus on XR products and applications
  • develop innovative computer vision and machine learning technology focused on 3D reconstruction, pose estimation, and novel view synthesis for XR applications
  • drive the research, development, and optimization of 3D scene representation pipelines
  • improving model efficiency, training stability, and visual fidelity for large-scale scenes
  • publication record in AI conferences