Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience in software development, and with data structures/algorithms.
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
• 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• Experience with performance analysis and GPU programming.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, a related technical field, or equivalent practical experience.
• 5 years of experience working in a complex, matrixed organization.
• Experience with machine learning systems (e.g., background theory, TensorFlow, or other ML tools).
• Experience working on compiler optimizations or related fields.
• Experience with architecture analysis and optimization.

Experience working on compiler optimizations or related fields.

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $189,000-$284,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.The US base salary range for this full-time position is $189,000-$284,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Analyse Large Language Model (LLM) performance and optimizations for partner teams including Google Gemini, Search, Cloud LLM and Application programming interfaces (APIs).
• Identify and maintain LLM training and serving benchmarks, and use them to identify performance opportunities and drive XLA:GPU/Triton performance and to guide future XLA releases.
• Engage with Google product teams, to solve their ML model performance challenges, including onboarding new LLM models and products onto Google’s GPU hardware and enabling LLMs to train efficiently on a very large scale (i.e., thousands of GPUs).
• Run architecture-level simulations on GPU designs and perform roofline analysis to guide partner teams.
• Analyze performance and efficiency metrics to identify bottlenecks, design, and implement solutions.