Senior Staff Software Engineer, Generative Ai, Search Intelligence

Google Google · Big Tech · Mountain View, CA +1

Senior Staff Software Engineer focused on Generative AI within Google Search. The role involves developing AI-driven tools for self-improvement, feedback-driven model training, and automated evaluations of Search features, including AI Mode and AI Overviews. Responsibilities include advocating for modeling and optimization techniques for LLMs, guiding user-signal-driven LLM improvements, partnering with research teams, and demonstrating expertise in LLM system design and ML modeling.

What you'd actually do

  1. Advocate the development of modeling, tuning, and optimization techniques to support large language model (LLM) performance, quality, and capabilities.
  2. Guide the design and implementation of user-signal-driven improvements of LLMs to address known loss patterns and response quality behaviors.
  3. Partner with experts from Google Research and DeepMind to advance LLM technologies and integrate them into groundbreaking products.
  4. Demonstrate exceptional teamwork, sharing your expertise in system design, machine learning modeling, and coding practices tailored to the LLM domain.
  5. Cultivate relationships with stakeholders across the organization to collaboratively define and execute LLM initiatives.

Skills

Required

  • software development
  • technical project strategy
  • ML design
  • optimizing industry-scale ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • design and architecture
  • testing/launching software products
  • 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 role
  • complex, matrixed organization
  • cross-functional, or cross-business projects

What the JD emphasized

  • leading technical project strategy, ML design, and optimizing industry-scale ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning)
  • state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision)

Other signals

  • LLM performance
  • user-signal-driven improvements
  • automated evaluations
  • AI Mode
  • AI Overviews