Senior Staff Software Engineer, Applied AI

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

Senior Staff Software Engineer role focused on designing, developing, and deploying large-scale AI solutions, with a specific emphasis on language models and agentic systems. The role involves technical leadership, ML infrastructure optimization, and applying the latest AI technologies to create impactful products. Requires extensive experience in software development, ML design, and industry ML infrastructure.

What you'd actually do

  1. Design, develop, test, deploy, maintain, and enhance large-scale software solutions.
  2. Provide technical leadership on high-impact projects by managing project priorities, deadlines, and deliverables.
  3. Drive alignment and clarity across teams on goals, outcomes, and timelines, while influencing and coaching a distributed team of engineers.
  4. Architect technical project strategy, lead large-scale ML infrastructure optimization, and oversee the design and implementation of solutions across multiple specialized ML areas.
  5. Stay up-to-date on the latest AI technologies, particularly in language models and agentic systems, and apply them to our technical solutions.

Skills

Required

  • software development
  • technical project management
  • ML design
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • Speech/audio
  • reinforcement learning
  • ML infrastructure
  • design and architecture
  • testing and launching software products
  • Machine Learning
  • AI Algorithms

Nice to have

  • data structures
  • algorithms
  • technical leadership
  • Natural Language Processing (NLP)
  • Large Language Model (LLM)
  • Computer Vision (CV)
  • Android development
  • mobile development

What the JD emphasized

  • large-scale software solutions
  • technical leadership
  • large-scale ML infrastructure optimization
  • design and implementation of solutions across multiple specialized ML areas
  • language models and agentic systems
  • Speech/audio
  • reinforcement learning
  • ML infrastructure
  • design and architecture

Other signals

  • large-scale ML infrastructure optimization
  • design and implementation of solutions across multiple specialized ML areas
  • language models and agentic systems