Machine Learning Engineer, Cv

Snap Snap · Consumer · Vienna, Austria

Machine Learning Engineer focused on Computer Vision for next-generation Spectacles, developing and deploying ML models for real-world AR applications.

What you'd actually do

  1. Develop novel technologies for the next generation of Spectacles.
  2. Explore and advance state-of-the-art machine learning and computer vision algorithms.
  3. Develop and deploy machine learning models.
  4. Work together with our cross-functional engineering and research teams in computer vision, machine learning and graphics.

Skills

Required

  • Deep understanding of machine learning principles, solutions and frameworks to develop networks and models for computer vision tasks
  • Ability to understand, debug and improve existing code as well as develop new algorithms using advanced computer vision and machine learning techniques.
  • Strong communications and interpersonal skills.
  • Bachelors’ degree in a technical field such as computer science, mathematics or equivalent experience.
  • 3+ years of research or engineering experience with machine learning approaches, in one or more of the following areas: hand/body tracking, object detection, object pose tracking, scene understanding (segmentation, classification), neural scene representation.
  • Experience with machine learning frameworks (PyTorch, TensorFlow etc.), as well as with cloud environments (GC, AWS etc).
  • Experience with software development in Python or C++

Nice to have

  • Msc/Phd in related field (Computer Vision, Machine Learning)
  • Experience in integrating Machine Learning models into Augmented Reality solutions
  • Experience in geometric computer vision such as SLAM, VIO, Tracking, multi-view 3D reconstruction, Depth Estimation etc.
  • Experience in neural network optimization (pruning, quantization, distillation) to deploy efficient models to resource-constrained devices.
  • Experience using AI tools to support engineering workflows (coding assistance, debugging, prototyping, data analysis, or experiment acceleration)

What the JD emphasized

  • hand/body tracking
  • object detection
  • object pose tracking
  • scene understanding (segmentation, classification)
  • neural scene representation
  • resource-constrained devices

Other signals

  • Develop and deploy machine learning models
  • Develop novel technologies for the next generation of Spectacles
  • Explore and advance state-of-the-art machine learning and computer vision algorithms