Software Engineer

Google Google · Big Tech · New York, NY +1

Software Engineer 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. Requires experience in ML, software development, data structures, algorithms, and ML infrastructure management.

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

  • Bachelor’s degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics or a related field
  • 2 years of experience in the job offered or in a Software Engineer-related occupation
  • Applying machine learning, statistics, or diffusion model theory in applied research
  • Software development using Java, C, C++, Python, or Go
  • Designing and applying data structures or algorithms
  • ML infrastructure management including model evaluation and data processing
  • Data analysis and synthesis to generate solutions or evaluate outcomes for machine learning applications

Nice to have

  • curating datasets
  • building ML pipelines
  • generative media
  • multimodal understanding
  • reinforcement learning
  • integration testing
  • performance testing
  • security testing
  • system qualification
  • monitoring
  • process automation
  • technical debt reduction

What the JD emphasized

  • Applying machine learning, statistics, or diffusion model theory in applied research
  • ML infrastructure management including model evaluation and data processing
  • Data analysis and synthesis to generate solutions or evaluate outcomes for machine learning applications

Other signals

  • prototyping GenAI solutions
  • building ML pipelines
  • applying machine learning
  • diffusion model theory
  • ML infrastructure management