Software Engineering Manager Ii, Ai/ml Recommendations, Rankings, Predictions, Youtube

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

Software Engineering Manager II for AI/ML Recommendations, Rankings, and Predictions at YouTube. This role involves leading a team of engineers, setting team priorities, developing technical roadmaps, designing and implementing recommendation systems, optimizing ML infrastructure, and guiding model architecture development. Requires experience in software development, ML design, ML infrastructure optimization, building and deploying recommendation systems, and technical/people leadership.

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 recommendation systems, optimize ML infrastructure, and guide the development of model architecture.

Skills

Required

  • software development
  • ML design
  • ML infrastructure optimization
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • recommendation systems
  • retrieval
  • prediction
  • ranking
  • embedding
  • architecture building
  • technical leadership
  • people management

Nice to have

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

What the JD emphasized

  • leading ML design
  • optimizing ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • building and deploying recommendation systems models
  • retrieval
  • prediction
  • ranking
  • embedding
  • building architecture

Other signals

  • recommendation systems
  • ML infrastructure
  • model architecture
  • people management