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

Google Google · Big Tech · Sunnyvale, CA +1

Software Engineering Manager II for Google Cloud's AI/ML GenAI team, focusing on leading teams to deliver AI and Infrastructure at scale. The role involves setting team priorities, developing technical vision and roadmaps, guiding system designs, and leading the design of GenAI solutions, optimizing ML infrastructure, and guiding data preparation and model optimization strategies. Requires significant experience in software development, ML infrastructure optimization, technical leadership, and GenAI techniques.

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 of GenAI solutions, optimize ML infrastructure, and guide the development of data preparation and model optimization strategies.

Skills

Required

  • software development
  • Python
  • C++
  • Java
  • JavaScript
  • ML design
  • ML infrastructure optimization
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • technical leadership
  • GenAI techniques
  • LLMs
  • Multi-Modal
  • Large Vision Models
  • language modeling
  • computer vision
  • people management
  • team leadership

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field
  • complex, matrixed organization
  • cross-functional projects
  • cross-business projects

What the JD emphasized

  • optimize ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • GenAI techniques
  • LLMs
  • Multi-Modal
  • Large Vision Models
  • language modeling
  • computer vision

Other signals

  • Google Cloud
  • AI and Infrastructure team
  • GenAI
  • ML infrastructure
  • TPUs
  • Vertex AI