Computer Vision SLAM Engineer

About the Role

3D Computer Vision Engineer

Key Responsibilities

• Develop and implement

  3D reconstruction pipelines

  from multi-view images, RGB-D, stereo, LiDAR, or photogrammetry data.
• Research, adapt, and optimize algorithms for

  SLAM

  ,

  SfM (Structure from Motion)

  , and

  MVS (Multi-View Stereo)

  .
• Build robust

  camera calibration

  and

  sensor fusion

  methods for RGB, depth, and IMU data.
• Integrate

  mesh generation

  ,

  texturing

  , and

  surface reconstruction

  techniques into production-ready systems.
• Collaborate with the AI, robotics, and 3D graphics teams to enhance reconstruction accuracy, speed, and scalability.
• Benchmark and evaluate algorithm performance on large-scale datasets.
• Stay updated on the latest advancements in computer vision, graphics, and 3D AI.

Qualifications

Required:

• Bachelor’s or Master’s in

  Computer Vision, Computer Science, Electrical Engineering

  , or related field.
• 3+ years of hands-on experience in

  3D reconstruction

  projects.
• Strong proficiency in

  Python

  and

  C++

  for high-performance computer vision.
• Experience with

  OpenCV

  ,

  Open3D

  ,

  PCL (Point Cloud Library)

  , or similar.
• Solid understanding of

  geometry, linear algebra, and optimization

  for 3D vision.
• Familiarity with

  camera calibration

  and

  pose estimation

  methods.

Preferred:

• PhD in a relevant field or equivalent research experience.
• Experience with

  Neural Radiance Fields (NeRF)

  ,

  Gaussian Splatting

  , or

  deep learning-based reconstruction

  .
• Hands-on work with

  3D graphics engines

  (Unity, Unreal) or WebGL.
• Experience in GPU acceleration with

  CUDA

  or

  TensorRT

  .
• Background in

  AR/VR, robotics, or autonomous systems

  .

Tools & Technologies

• Programming:

  Python, C++, CUDA

• Libraries/Frameworks:

  OpenCV, Open3D, PCL, PyTorch, TensorFlow, COLMAP, Meshroom

• 3D Data Formats:

  OBJ, PLY, GLTF, USDZ

• Version Control:

  Git, GitHub/GitLab

• Cloud & Deployment:

  AWS, Docker, CI/CD pipelines