Software Engineer

Google Google · Big Tech · Sunnyvale, CA +1

Software Engineer role at Google DeepMind focused on applying research to high-impact GenAI problems. Responsibilities include prototyping GenAI solutions, building ML pipelines for generative media, multimodal understanding, and reinforcement learning, developing robust product code, and managing the full deployment lifecycle. Requires experience in software development, data structures, algorithms, ML infrastructure management, and applied research project lifecycles.

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, or Go
  • Designing and applying data structures or algorithms
  • Systems thinking or analyzing technical problems from a broad, systems-level perspective
  • ML infrastructure management including model deployment, evaluation, optimization, and data processing
  • Managing the full lifecycle of applied research projects from proof-of-concept to implementation

Nice to have

  • Generative media
  • Multimodal understanding
  • Reinforcement learning

What the JD emphasized

  • ML infrastructure management including model deployment, evaluation, optimization, and data processing
  • Managing the full lifecycle of applied research projects from proof-of-concept to implementation

Other signals

  • Generative AI
  • ML pipelines
  • Applied research
  • Model deployment
  • Optimization