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

Google Google · Big Tech · San Bruno, CA +1

Senior Software Engineering Manager for AI/ML Recommendations, Rankings, and Predictions at YouTube. This role involves setting team priorities, developing technical vision and roadmaps, overseeing system designs, and driving technical project strategy, including large-scale ML infrastructure optimization and the design/implementation of advanced recommendation systems. Requires significant experience in software development, technical leadership, people management, and building/deploying recommendation systems models in production.

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 long-term technical vision and roadmap within, and often beyond, the scope of your teams. Evolve the roadmap to meet anticipated future requirements and infrastructure needs.
  4. Oversee systems designs within the scope of the broader area, and review product or system development code to solve ambiguous problems.
  5. Drive technical project strategy, lead large-scale ML infrastructure optimization, and oversee the design and implementation of advanced recommendation systems.

Skills

Required

  • software development
  • technical leadership
  • people management
  • ML design
  • ML infrastructure optimization
  • recommendation systems
  • system design

Nice to have

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

What the JD emphasized

  • leading large-scale ML infrastructure optimization
  • design and implementation of advanced recommendation systems
  • building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production

Other signals

  • managing ML engineers
  • leading large-scale ML infrastructure optimization
  • design and implementation of advanced recommendation systems