Software Engineer

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

Software Engineer role at Google DeepMind focused on applying research to high-impact GenAI problems, including prototyping solutions, curating datasets, and building ML pipelines for generative media, multimodal understanding, and reinforcement learning. The role involves developing and testing robust product code, collaborating with peers, 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

  • iOS development (Swift/Objective-C)
  • front-end feature implementation
  • collaboration with UX/Product Management
  • Designing and applying data structures or algorithms
  • Systems thinking or analyzing technical problems from a broad, systems-level perspective
  • Designing and executing live experiments
  • evaluating performance data
  • Managing the full lifecycle of applied research projects

Nice to have

  • GenAI solutions prototyping
  • ML pipelines
  • generative media
  • multimodal understanding
  • reinforcement learning
  • integration testing
  • performance testing
  • security testing
  • debugging
  • root cause analysis
  • technical documentation
  • system qualification
  • monitoring
  • process automation
  • technical debt management

What the JD emphasized

  • applied research projects from proof-of-concept to implementation

Other signals

  • prototyping GenAI solutions
  • building ML pipelines
  • applied research projects