Research Software Engineer, ML Efficiency, Google Research

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
  
• 1 year of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
  
• 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).

Preferred qualifications:

• PhD in Machine Learning, AI, Computer Science, Statistics, Applied Mathematics, Data Science, or related technical fields.
• Experience in a university or industry labs, with emphasis on AI research.
• Experience in theoretical and empirical research and solving impactful research problems.
• Understanding of Transformer architecture internals.
• Publication record in top AI venues.

At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.

In this role, you will be making significant breakthroughs towards Computational Efficiency of Generative AI Models (e.g., LLMs, Diffusion Models, Generative Videos). You will deliver research on algorithmic efficiency, model compression, and inference acceleration, impacting how next-generation AI models will be deployed to people.

• Write product or system development code. 
  
• 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).
  
• Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
  
• 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.
  
• Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.