Senior Software Engineer, Ai/ml Infrastructure, Ads

Google Google · Big Tech · Pittsburgh, PA +1

Senior Software Engineer role focused on AI/ML Infrastructure for Google Ads. The role involves designing and implementing solutions in specialized ML areas, leveraging ML infrastructure, and demonstrating expertise in fields like speech/audio or reinforcement learning. Key responsibilities include writing and testing code, collaborating on design and code reviews, triaging issues, and debugging. Minimum qualifications require significant experience in software development, testing, and ML infrastructure, including areas like model deployment, evaluation, optimization, data processing, and debugging.

What you'd actually do

  1. Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.
  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. 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.
  5. Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.

Skills

Required

  • software development
  • software design and architecture
  • testing software products
  • maintaining software products
  • launching software products
  • ML infrastructure
  • model deployment
  • model evaluation
  • optimization
  • data processing
  • debugging
  • Speech/audio
  • reinforcement learning

Nice to have

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

What the JD emphasized

  • specialized ML areas
  • ML infrastructure
  • model deployment
  • model evaluation
  • optimization
  • data processing
  • debugging
  • Speech/audio
  • reinforcement learning

Other signals

  • ML infrastructure
  • model deployment
  • model evaluation
  • optimization
  • data processing
  • debugging
  • speech/audio
  • reinforcement learning