Software Engineering Manager, Machine Learning, Kids and Families

Google Google · Big Tech · Kirkland, WA +2

Google is seeking a Software Engineering Manager for their Kids and Families team. This role involves technical leadership, people management, and driving the execution and deployment of ML models, data pipelines, and training infrastructure. The team focuses on building new services and experiences for minors and families, ensuring compliance with global regulations, and integrating with other Google products like YouTube and Gemini. Experience with LLMs, NLP, Generative AI, and working with regulatory constraints is crucial.

What you'd actually do

  1. Work with Technical Leads to establish and drive a technical road map for your team, mapping business objectives to a cohesive team strategy. Lead the execution and deployment of sophisticated ML models.
  2. Foster a healthy, high-performing team culture. Manage career growth, performance, and mentorship for engineers on your team.
  3. Collaborate cross-functionally with peer teams across Search, YouTube, and Core. Partner with Data Science, Product, and Policy to define model behaviors that respect complex regulatory and privacy constraints.
  4. Guide the team in designing and building scalable data pipelines and training infrastructure for large-scale datasets. Ensure long-term system reliability and robustness by integrating modern ML infrastructure and engineering best practices.
  5. Align team priorities with broader organization goals and company-wide objectives. Identify and mitigate project risks, manage dependencies, and reduce operational toil across functional lines.

Skills

Required

  • software development
  • system design
  • people management
  • machine learning
  • recommendation systems
  • natural language processing
  • computer vision
  • pattern recognition
  • artificial intelligence
  • Large Language Models
  • NLP
  • Generative AI

Nice to have

  • leading managers or tech leads
  • Trust and Safety
  • user classification
  • fraud/risk detection
  • identity verification
  • adversarial machine learning
  • building classifiers
  • ranking
  • research background
  • Data Scientists
  • model quality
  • calibration models
  • evaluation methods
  • de-biasing techniques
  • mentoring junior engineers

What the JD emphasized

  • technical leadership
  • manage engineers
  • ML models
  • data pipelines
  • training infrastructure
  • large-scale datasets
  • regulatory and privacy constraints
  • Large Language Models
  • NLP
  • Generative AI

Other signals

  • managing engineers
  • technical leadership
  • ML models
  • data pipelines
  • training infrastructure
  • large-scale datasets
  • LLMs
  • Generative AI
  • regulatory and privacy constraints