Senior Software Engineer, Generative Ai, Gup

Google Google · Big Tech · Sunnyvale, CA +1

This role focuses on designing, developing, testing, deploying, and maintaining large software systems for user feedback model serving and an AI assistant for customer support. It involves prototyping, managing project priorities, collecting and analyzing data for ML models, and collaborating with cross-functional teams. The role requires experience with GenAI techniques and ML infrastructure, with a focus on deploying and serving ML models.

What you'd actually do

  1. Design, develop, test, deploy and maintain large software systems for user feedback model serving, Cases AI assistant and other ML based support needs. Prototype and build features and solutions in a fast-moving environment.
  2. Manage individual and project priorities, deadlines and deliverables. Be a part of 24x7 support rotation.
  3. Collect and analyze data for training, understanding and evaluation to understand larger recurring problems, and solve them.
  4. Collaborate closely with Engineering, Product Management, and UX to complete tasks end to end with minimal guidance from executive leads, and influence product requirements.
  5. Review code developed by other engineers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).

Skills

Required

  • software development in Java
  • GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models)
  • GenAI-related concepts (language modeling, computer vision)
  • testing, maintaining, or launching software products
  • software design and architecture
  • ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging)

Nice to have

  • Master's degree or PhD in Computer Science or related technical field
  • software development in one or more programming languages (e.g. C++, Java, Python)
  • large scale data processing and ML platforms
  • quality analysis and improvement using relevant tools (A/B testing, experiment frameworks, data analysis)
  • problem-solving, critical thinking and investigative skills
  • communication and teamwork skills with the ability to collaborate across boundaries

What the JD emphasized

  • large software systems
  • ML based support needs
  • ML infrastructure
  • GenAI techniques
  • Large Vision Models

Other signals

  • ML based support needs
  • user feedback model serving
  • Cases AI assistant
  • ML infrastructure
  • GenAI techniques