Senior Software Engineer, Ai/ml Computer Vision, Google Cloud

Google Google · Big Tech · Bengaluru, Karnataka, India

Senior Software Engineer focused on AI/ML Computer Vision within Google Cloud, responsible for designing, implementing, and testing computer vision systems, leveraging ML infrastructure, and evaluating algorithms. Requires experience in Python/C++, Computer Vision, ML infrastructure, and software design.

What you'd actually do

  1. Design and implement computer vision systems, leverage ML infrastructure, and evaluate tradeoffs between different algorithms and design techniques.
  2. Write and test product or system development code.
  3. Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency,)
  4. Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
  5. Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.

Skills

Required

  • Python or C++
  • Computer Vision (image classification and processing, object detection, visual search), video generation, or signal processing
  • designing Computer Vision systems
  • ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging)
  • testing, maintaining, or launching software products
  • software design and architecture

Nice to have

  • Master's degree or PhD in Computer Science or related technical field
  • data structures/algorithms
  • technical leadership role
  • developing accessible technologies

What the JD emphasized

  • Computer Vision systems
  • ML infrastructure

Other signals

  • design and implement computer vision systems
  • leverage ML infrastructure
  • evaluate tradeoffs between different algorithms and design techniques
  • 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging)