GPU Performance Modeling Engineer

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.
• 2 years of experience with data structures or algorithms in either an academic or industry setting.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical fields.
• 3 years of experience with LLMs/ML, algorithms and tools (e.g. TensorFlow/Jax), Artificial Intelligence (AI), deep learning, or natural language processing.
• 2 years of experience building and developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage, or hardware architecture.
• Experience in developing and deploying AI/ML models and algorithms.
• Experience in Python and any other languages (e.g., C++, Kotlin, Java.).
• Understanding of Machine Learning, data analysis and developer tools.

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.

Graphics Processing Unit (GPU) Performance team is responsible for optimizing, modeling and evaluating GPU systems for comparative analysis and benchmarking of Google’s ML (used internally and by external Google Cloud customers) workloads/hardware. This team strives for extracting maximum efficiency in Google’s GPU fleet.

In this role, you will evaluate current and future ML workloads/hardware using detailed benchmarking and simulation of ML systems and guide decision making for the Cloud hardware teams and cross-functional optimization efforts to improve GPU fleet efficiency.

The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

The US base salary range for this full-time position is $141,000-$202,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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

• Help Google Cloud thoroughly evaluate the performance of future GPU platforms with an opportunity to influence the GPU roadmap at Google.
  
• Engage with GPU vendors to perform a detailed benchmark of the latest GPU systems and improve the simulation accuracy for these new systems.
  
• Perform detailed roofline analysis on the latest production ML workloads/hardware to help identify opportunities/bottlenecks for optimization in the fleet.
  
• Conduct competitive analysis of various Machine Learning (ML) workloads/platforms to better understand and help Google leadership navigate the complex and ever-changing ML landscape.