Senior Staff Software Engineer, Ai/ml, Google Workspace

Google Google · Big Tech · Sunnyvale, CA +1

Senior Staff Software Engineer at Google, focusing on AI/ML for Google Workspace products. The role involves designing, developing, and deploying large-scale AI/ML solutions to enhance productivity tools like Gmail, Docs, and Meet. Responsibilities include technical leadership, ML infrastructure optimization, and driving technical project strategy, with a focus on shipping AI-powered features to billions of users.

What you'd actually do

  1. Design, develop, test, deploy, maintain, and enhance large scale software solutions.
  2. Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.
  3. Facilitate alignment and clarity across teams on goals, outcomes, and timelines. Influence and coach a distributed team of engineers.
  4. Drive technical project strategy, lead large-scale ML infrastructure optimization, and oversee the design and implementation of solutions across multiple specialized ML areas.

Skills

Required

  • software development
  • technical project strategy
  • ML design
  • ML infrastructure optimization
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • design and architecture
  • testing/launching software products
  • Speech/audio
  • reinforcement learning
  • ML infrastructure
  • ML field specialization

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  • data structures/algorithms
  • technical leadership role leading project teams and setting technical direction
  • cross-functional, or cross-business projects

What the JD emphasized

  • 7 years of experience leading technical project strategy, ML design, and optimizing industry-scale ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  • 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.

Other signals

  • AI will change the future of work
  • build how productivity tools should work 5-10 years into the future
  • impact billions of users across the world