Software Engineering Manager Ii, Ai/ml, Google Cloud Compute

Google Google · Big Tech · Kirkland, WA +3

Software Engineering Manager II for Google Cloud Compute, responsible for leading teams, setting technical vision, and overseeing the design and implementation of ML solutions, including ML infrastructure optimization and model development strategies. Requires strong software development and ML experience, with a focus on leadership and people management.

What you'd actually do

  1. Set and communicate team priorities that support the broader organization's goals. Align strategy, processes, and decision-making across teams.
  2. Set clear expectations with individuals based on their level and role and aligned to the broader organization's goals. Meet regularly with individuals to discuss performance and development and provide feedback and coaching.
  3. Develop the mid-term technical vision and roadmap within the scope of your (often multiple) team(s). Evolve the roadmap to meet anticipated future requirements and infrastructure needs.
  4. Design, guide and vet systems designs within the scope of the broader area, and write product or system development code to solve ambiguous problems.
  5. Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.

Skills

Required

  • software development
  • Python
  • C++
  • Java
  • JavaScript
  • Speech/audio
  • reinforcement learning
  • ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • technical leadership
  • people management

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  • cross-functional project experience
  • cross-business project experience

What the JD emphasized

  • 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.
  • 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).

Other signals

  • manage engineers across multiple teams and locations
  • large product budget
  • oversee the deployment of large-scale projects
  • optimize ML infrastructure
  • guide the development of model optimization and data processing strategies