Software Engineer Iii, Ai/ml, Google Cloud, Automotive

Google Google · Big Tech · Zürich, Switzerland

Software Engineer III role at Google Cloud focusing on AI/ML within the Automotive domain. Responsibilities include writing product/system code, collaborating on best practices, contributing to documentation, triaging issues, and implementing ML solutions with a focus on ML infrastructure, model optimization, and data processing. Requires experience in speech/audio, reinforcement learning, or ML infrastructure.

What you'd actually do

  1. Write product or system development code.
  2. Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
  3. Contribute to existing documentation or educational content and adapt content based on product or program updates and user feedback.
  4. Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
  5. Implement solutions in one or more specialized Machine Learning (ML) areas, utilize ML infrastructure, and contribute to model optimization and data processing.

Skills

Required

  • Software development in one or more programming languages
  • Speech/audio technology
  • Reinforcement learning
  • ML infrastructure
  • Model deployment
  • Model evaluation
  • Model optimization
  • Data processing
  • Debugging ML systems

Nice to have

  • Data structures
  • Algorithms
  • Accessible technologies development

What the JD emphasized

  • 1 year of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
  • 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Other signals

  • ML infrastructure
  • model optimization
  • data processing
  • Speech/audio
  • reinforcement learning