Software Engineering Manager, GPU Performance

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
  
• 5 years of experience leading in Machine Learning (ML) design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  
• 3 years of experience in a technical leadership role, overseeing projects, with 2 years of experience in a people management, supervision/team leadership role.
  
• Experience with Performance Analysis, Graphics Processing Unit (GPU) Programming, Machine Learning algorithms and tools.
  

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 3 years of experience working in an organization involving cross-functional, or cross-business projects.
  
• Experience with Compute Unified Device Architecture (CUDA), Performance Optimization and GPU Hardware Architecture.
  

About the job

Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.

With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.

In this role, you will optimize, model and evaluate Graphics Processing Unit (GPU) systems for comparative analysis for Google’s internal Machine Learning (ML) workloads. You will extract efficiency in Google’s GPU fleet. You will focus on performance analysis and optimization to identify opportunities in Google production and research ML workloads and land optimization. You will evaluate ML workloads and run performance/Technical Case Owner simulations to collect roofline estimates and guide decision for the Cloud hardware teams. You will focus on Large Language Models (LLM) (Google Deepmind Gemini, Bard, Search Magi, Cloud LLM Application programming interfaces, etc.) performance analysis and optimizations for GPU.

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.

Responsibilities

• Set and communicate team priorities that support the broader organization's goals. Align strategy, processes, and decision-making across teams.
  
• Set clear expectations with individuals based on their level and role. Meet with individuals to discuss performance, development, and provide feedback and coaching.
  
• Develop the technical goal and roadmap within the scope of your team(s). Evolve the roadmap to meet anticipated future requirements and infrastructure needs.
  
• Lead the design and implementation of solutions in specialized Machine Learning (ML) areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.
  
• Maintain Large Language Models (LLM) training and serving benchmarks that are representative to Google production, industry and ML community, use them to identify performance opportunities and drive Accelerated Linear Algebra (XLA):GPU/Triton performance toward state-of-the-art, and to guide XLA releases.