Senior Research Engineer, Autonomous Driving Simulation

Google Google · Big Tech · Haifa, Israel

This role focuses on designing and implementing solutions in specialized ML areas, leveraging ML infrastructure, with a strong emphasis on reinforcement learning and ML infrastructure components like model deployment, evaluation, and data processing for autonomous driving simulation.

What you'd actually do

  1. Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.
  2. Write and test product or system development code.
  3. 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).
  4. Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
  5. 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.

Skills

Required

  • Software development
  • Software testing
  • Software architecture
  • Speech/audio
  • Reinforcement learning
  • ML infrastructure
  • Model deployment
  • Model evaluation
  • Data processing
  • Debugging

Nice to have

  • Master's degree or PhD in Computer Science
  • Data structures
  • Algorithms
  • Technical leadership
  • Accessible technologies

What the JD emphasized

  • 5 years of experience with software development in one or more programming languages
  • 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture
  • 3 years 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.
  • 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging)

Other signals

  • ML infrastructure
  • reinforcement learning
  • data processing
  • model deployment
  • model evaluation