Software Engineer

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

Software Engineer at Google DeepMind focused on applying research to high-impact GenAI problems, curating datasets, and building ML pipelines for generative media, multimodal understanding, and reinforcement learning. Responsibilities include developing robust product code, performing comprehensive testing, collaborating with peers, triaging and resolving complex system issues, and managing the full deployment lifecycle.

What you'd actually do

  1. Apply research to high-impact problems by prototyping GenAI solutions, curating datasets, and building ML pipelines for generative media, multimodal understanding, and reinforcement learning.
  2. Develop and test robust product code, performing comprehensive testing that includes integration, performance, and security to ensure system quality and reliability.
  3. Collaborate with peers through rigorous design and code reviews to enforce best practices, improve system testability, and ensure overall efficiency and accuracy.
  4. Triage and resolve complex system issues by debugging, analyzing root causes, and implementing solutions to optimize hardware, network, and service operations.
  5. Create and maintain technical documentation and educational materials, adapting content based on product updates and user feedback to ensure clarity and relevance. Manage the full deployment lifecycle by contributing to system qualification, monitoring, process automation, and paying down technical debt to improve long-term scalability.

Skills

Required

  • Software development using Java, C, C++, Python, Go or Kotlin
  • Designing and applying data structures or algorithms
  • Systems thinking or analyzing technical problems from a broad, systems-level perspective
  • Software testing across the development lifecycle including validity, verification, performance, and reliability
  • Root cause analysis for debugging software and ML systems

Nice to have

  • GenAI solutions prototyping
  • ML pipelines for generative media
  • Multimodal understanding
  • Reinforcement learning
  • Integration testing
  • Performance testing
  • Security testing
  • System debugging
  • Technical documentation

What the JD emphasized

  • Software development using Java, C, C++, Python, Go or Kotlin
  • Designing and applying data structures or algorithms
  • Systems thinking or analyzing technical problems from a broad; systems-level perspective
  • Software testing across the development lifecycle including validity, verification, performance, and reliability
  • Root cause analysis for debugging software and ML systems

Other signals

  • applying research to high-impact problems
  • prototyping GenAI solutions
  • building ML pipelines for generative media, multimodal understanding, and reinforcement learning