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Verkada

Software Engineer - Computer Vision

San Mateo, CA United StatesFrom $300kfull-timemidAdded today

About this role

Verkada seeks a Software Engineer for its Computer Vision team to develop AI and ML models powering advanced video analytics. You'll work on detection algorithms, edge deployment, and real-time processing across millions of deployed devices.

What you'll do

  • Develop and optimize computer vision algorithms for detection, recognition, and scene analysis
  • Train deep learning models using PyTorch, TensorFlow, or Keras for tasks like people/vehicle detection and license plate recognition
  • Write clean, modular C++ code for production systems
  • Implement and deploy models on edge devices for real-time processing
  • Collaborate with agile teams on features such as facial recognition and object frequency counting
  • Work on data structures and system architecture for large-scale video analytics

What they're looking for

  • C++ programming
  • Deep learning frameworks (PyTorch, TensorFlow, Keras)
  • Computer vision algorithms
  • Python or other practical programming languages
  • Edge device deployment
  • Data structures and software architecture
  • Machine learning model training and optimization

Benefits

  • 100% employee health insurance premiums covered; 80% family premiums
  • Medical, vision, and dental coverage nationwide
  • Flexible PTO, paid holidays, and parental leave
  • Professional development stipend
  • Daily healthy lunches and fitness benefits
  • Mental health support and wellness resources
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Verkada

Verkada builds cloud-managed, AI-driven security solutions for enterprise customers, including physical security and building management products. The company is hiring Technical Support Engineers to troubleshoot issues and assist customers, as well as Solutions Engineers to provide pre-sales support and design tailored solutions.

Website
verkada.com
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Likely interview questions

  • Walk us through a computer vision project where you trained and deployed a deep learning model—what challenges did you face and how did you overcome them?
  • How have you optimized inference performance for edge devices, and what trade-offs between accuracy and latency have you made?
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