Software Engineer Iii, Ai/ml, Gup Customer Support

Google Google · Big Tech · Sunnyvale, CA +1

Software Engineer III at Google working on User Voice and Cases AI Assistant, focusing on designing, developing, testing, deploying, and maintaining software systems for user voice model serving and other ML-based support needs. The role involves integrating ML models into production environments and working with ML infrastructure.

What you'd actually do

  1. Design, develop, test, deploy and maintain large software systems for user voice model serving and other ML based support needs.
  2. Prototype and build features and solutions in a changing environment.
  3. Collaborate closely with Engineering, Product Management, and UX to complete tasks end-to-end with minimal guidance from executive leads, and influence product requirements.
  4. Manage individual and project priorities, deadlines and deliverables. Review code developed by other engineers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
  5. Be a part of 24x7 support rotation with strict service level objectives (SLOs).

Skills

Required

  • Java
  • Typescript
  • software development
  • programming
  • coding
  • design
  • debugging
  • integrating machine learning models in production environments
  • ML infrastructure
  • model deployment
  • model evaluation
  • optimization
  • data processing
  • debugging
  • collaborating with cross-functional engineering teams
  • documentation for external technical audiences

Nice to have

  • C++
  • Python
  • Generative AI
  • Agentic AI technologies
  • consumer products
  • customer support
  • next generation technologies
  • investigative skills
  • problem-solving skills
  • critical thinking skills
  • communication skills
  • teamwork skills

What the JD emphasized

  • integrating machine learning models in production environments
  • ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging)

Other signals

  • ML model serving
  • ML based support needs
  • integrate ML models in production
  • ML infrastructure