Senior Software Engineer, Ai/ml, Google Workspace

Google Google · Big Tech · Sunnyvale, CA +1

Senior Software Engineer to deliver and develop machine learning technologies for Google Workspace products (Gmail, Chat, Calendar). This role involves collaborating with researchers and product managers to build AI-powered productivity tools, focusing on generative AI agents and LLM applications, with a direct impact on billions of users.

What you'd actually do

  1. Write and test product or system development code.
  2. 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).
  3. 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.
  4. Analyze and interpret data to improve model/system performance and identify areas for improvement.
  5. Leverage models and solutions with technologies to solve problems. Experiment with various model architectures and tuning methodologies.

Skills

Required

  • software development
  • software design and architecture
  • ML infrastructure
  • model deployment
  • model evaluation
  • optimization
  • data processing
  • debugging
  • generative AI agents
  • LLM application workflows
  • prototyping
  • proof-of-concept models

Nice to have

  • technical leadership
  • accessible technologies

What the JD emphasized

  • delivering machine learning technologies to Gmail/Chat/Calendar customers
  • developing challenging machine learning solutions
  • designing, building, or deploying generative AI agents or application workflows using Large Language Models (LLMs)
  • 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging)
  • Experience designing and building prototypes or proof-of-concept models to validate product ideas.

Other signals

  • delivering machine learning technologies to customers
  • developing challenging machine learning solutions
  • building how productivity tools should work 5-10 years into the future
  • designing, building, or deploying generative AI agents or application workflows using Large Language Models (LLMs)