Staff Software Engineer, Core, Ai/ml Quality

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

Staff Software Engineer focused on improving the quality of an internal AI assistant used by Googlers. The role involves exploring advanced techniques for LLMs and agentic harnesses, working with partner teams on feedback training and model iteration, and delivering end-to-end solutions.

What you'd actually do

  1. Lead ambitious workstreams to improve agent quality.
  2. Explore advanced techniques for getting the best out of available Large Language Models (LLMs) and agentic harnesses.
  3. Provide insights and strategies to improve agent quality while staying informed on the latest developments in AI industry/research.
  4. Work with the Gemini-for-Google and Deepmind teams to run feedback training/evaluate data and iterate on model quality.
  5. Collaborate across cross-functional and partner teams to deliver end-to-end solutions.

Skills

Required

  • software development
  • testing
  • launching software products
  • software design
  • software architecture
  • ML design
  • ML infrastructure optimization
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • GenAI techniques
  • LLMs
  • Multi-Modal
  • Large Vision Models
  • language modeling
  • computer vision

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field
  • data structures
  • algorithms
  • technical leadership
  • project teams
  • setting technical direction
  • complex, matrixed organization
  • cross-functional projects
  • cross-business projects

What the JD emphasized

  • 8 years of experience in software development
  • 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture
  • 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning)
  • 2 years of experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision)

Other signals

  • AI assistant for internal users
  • improving agent quality
  • working with LLMs and agentic harnesses
  • feedback training and iterating on model quality
  • cross-functional collaboration for end-to-end solutions