Software Engineer

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

Software Engineer role at Google DeepMind focused on applying research to high-impact problems by prototyping GenAI solutions, curating datasets, and building ML pipelines for generative media, multimodal understanding, and reinforcement learning. The role involves developing robust product code, designing and building infrastructure for next-gen AI features, 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. Design and build infrastructure to support next-gen AI features
  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; and 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, or Go
  • Designing and applying data structures or algorithms
  • Systems thinking or analyzing technical problems from a broad, systems-level perspective
  • Managing the full lifecycle of applied research or machine learning projects from proof-of-concept to implementation
  • Data analysis and synthesis to generate solutions or evaluate outcomes for machine learning applications

What the JD emphasized

  • managing the full lifecycle of applied research or machine learning projects from proof-of-concept to implementation

Other signals

  • prototyping GenAI solutions
  • building ML pipelines
  • managing the full lifecycle of applied research or machine learning projects