Software Engineer Iii, Ai/ml Genai, Google Ads

Google Google · Big Tech · Mountain View, CA +1

Software Engineer III at Google Ads focused on implementing GenAI solutions, utilizing ML infrastructure, and contributing to data preparation, optimization, and performance enhancements. The role involves writing product or system development code, collaborating with peers, triaging issues, and debugging. Requires experience with core GenAI concepts like LLMs and Large Vision Models, and experience with text, image, video, or audio generation, along with ML infrastructure and programming in Python or C++.

What you'd actually do

  1. Implement GenAI solutions, utilize ML infrastructure, and contribute to data preparation, optimization, and performance enhancements.
  2. Write 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

  • Python or C++
  • ML infrastructure
  • model deployment
  • model evaluation
  • optimization
  • data processing
  • debugging
  • GenAI concepts
  • LLM
  • Multi-Modal
  • Large Vision Models
  • text generation
  • image generation
  • video generation
  • audio generation

Nice to have

  • Master's degree or PhD in Computer Science or related technical fields
  • data structures
  • algorithms
  • accessible technologies

What the JD emphasized

  • ML infrastructure
  • model deployment
  • model evaluation
  • optimization
  • data preparation
  • GenAI concepts
  • LLM
  • Large Vision Models
  • text, image, video, or audio generation

Other signals

  • GenAI
  • LLM
  • Large Vision Models
  • ML infrastructure
  • model deployment
  • model evaluation
  • optimization
  • data preparation