Senior Staff Research Engineer, Cloud AI Research

Google Google · Big Tech · Sunnyvale, CA +1

This role focuses on advancing AI research for Google Cloud, specifically improving foundation models, retrieval-augmented LLMs, and GenAI for multimodal applications to address enterprise needs. The role involves developing innovative ML/NLP techniques, publishing research, and collaborating with product teams to bring innovations to production.

What you'd actually do

  1. Develop innovative machine learning and natural language processing techniques in the directions that are of high impact to the organization, push important scenarios, and publish outcomes at conferences and journals.
  2. Collaborate with Google Cloud product teams to address customer needs and realize the business impact of research breakthroughs via Google Cloud AI products.
  3. Invent and implement generalizable methods that perform at scale across various real-world AI issues of enterprises.
  4. Engage with and learn from researchers and engineers across Google.

Skills

Required

  • software development
  • technical project strategy
  • ML design
  • 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
  • data structures/algorithms
  • technical leadership
  • cross-functional projects
  • publications in related research venues

What the JD emphasized

  • 7 years of experience leading technical project strategy, ML design, and working with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  • 5 years of experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI - related concepts (e.g., language modeling, computer vision).
  • Experience with publications in related research venues (e.g., CVPR, ICCV, NeurIPS, ICML, ICLR, etc).

Other signals

  • improving foundation models
  • retrieval-augmented LLMs
  • LLM-assisted search
  • GenAI for multimodal inputs and outputs
  • real-world AI issues of enterprises