Software Engineer

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

Software Engineer at Google DeepMind focused on applying research to build next-generation GenAI features. Responsibilities include prototyping GenAI solutions, curating datasets, building ML pipelines for generative media and multimodal understanding, developing product code, and performing comprehensive testing. Requires experience in developing ML models with Tensorflow/PyTorch/JAX and applying ML/statistics/diffusion model theory.

What you'd actually do

  1. Apply research to build next-generation features and solve 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

  • Developing machine learning models using Tensorflow, PyTorch, or JAX
  • 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
  • Data analysis and synthesis to generate solutions or evaluate outcomes for machine learning applications

Nice to have

  • prototyping GenAI solutions
  • curating datasets
  • building ML pipelines for generative media
  • multimodal understanding
  • reinforcement learning

What the JD emphasized

  • Developing machine learning models using Tensorflow, PyTorch, or JAX
  • 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
  • Data analysis and synthesis to generate solutions or evaluate outcomes for machine learning applications

Other signals

  • prototyping GenAI solutions
  • building ML pipelines
  • developing and testing robust product code
  • applying machine learning, statistics, or diffusion model theory