Software Engineer, Foundational ML Research

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

This role focuses on foundational ML research, prototyping algorithmic approaches, and designing/implementing solutions in specialized ML areas. It involves large-scale model training and applying research to post-training generative models.

What you'd actually do

  1. Prototype and test fundamental algorithmic approaches. Write and test product or system development code.
  2. Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency,) and bring solutions into production environments
  3. Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
  4. Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
  5. Design and implement solutions in one or more specialized ML areas such as reinforcement learning, optimization, privacy, and game theory.

Skills

Required

  • Python
  • training machine learning models

Nice to have

  • PhD in Computer Science or equivalent field
  • data structures/algorithms
  • publishing in venues (e.g., NeurIPS, ICML, COLT, ICLR) or contributing to open-source projects related to ML
  • ML frameworks (JAX, PyTorch) for large-scale model training
  • applying ML research to generative models, specifically in areas of post-training Gemini models

What the JD emphasized

  • training machine learning models
  • large-scale model training
  • post-training Gemini models

Other signals

  • foundational ML research
  • large-scale model training
  • post-training Gemini models