Research Software Engineer, Foundational ML Research

Google Google · Big Tech · Kirkland, WA +2

Research Software Engineer focused on Foundational ML Research, involving technical leadership, ML infrastructure optimization, model optimization, data processing, and prototyping algorithmic approaches. Requires experience in ML design, ML infrastructure, and specialized ML fields like speech/audio or reinforcement learning.

What you'd actually do

  1. Provide technical leadership on high-impact projects, managing project priorities, deadlines, and deliverables.
  2. Facilitate alignment and clarity across teams on goals, outcomes, and timelines, while influencing and coach a distributed team of engineers.
  3. Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.
  4. Prototype and test fundamental algorithmic approaches, and collaborate with researchers and partners to bring solutions into production environments.

Skills

Required

  • Computer Science
  • Artificial Intelligence
  • Machine Learning
  • software development
  • ML design
  • ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • Speech/audio
  • reinforcement learning
  • testing
  • launching software products
  • software design
  • architecture

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field
  • data structures and algorithms
  • technical leadership role
  • complex, matrixed organization
  • cross-functional projects
  • statistical modeling
  • machine learning algorithms

What the JD emphasized

  • ML design
  • ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • Speech/audio
  • reinforcement learning
  • testing
  • launching software products
  • software design
  • architecture

Other signals

  • Foundational ML Research
  • ML infrastructure
  • model optimization
  • data processing strategies
  • algorithmic approaches