Staff Software Engineer, TPU Performance, Hardware/Software Co-Design
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience testing, and launching software products.
- 5 years of experience with software development in one or more programming languages (e.g., Python, C, C++).
- Experience in performance analysis and optimization including system architecture, performance modeling, benchmarking, machine learning infrastructure, or similar.
Preferred qualifications:
- Master’s degree or PhD with an emphasis on Computer Architecture, Performance Analysis, or ML workloads and accelerator platforms (GPU/TPU).
- 3 years of experience working in a complex, matrixed organization including technical leadership role leading project teams and setting technical direction.
- Experience with hardware (e.g., how chips are built, RTL/VHDL/Verilog, interfaces (PCIe, Ethernet)).
- Experience with ML Accelerators and simulators, especially hardware simulators.
- Understanding of compiler flows, software involved in translating a high-level language (e.g., TensorFlow) to hardware instructions.
- Understanding of Large Language Models.
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.
The Machine Learning Hardware/Software Co-Design team is responsible for finding out what TPU/GPU Platform Google needs to build to meet tomorrow's AI goals. The team is responsible to do a full stack performance analysis of ML workloads such as Gemini on future TPU systems, evaluate performance/cost trade-offs of Hardware features and Software optimization techniques and drive the next generation TPU Chip and system architecture.
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 $197,000-$291,000 + 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
- Design, develop, test, deploy, maintain, and enhance large-scale software solutions. Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.
- Facilitate alignment and clarity across teams on goals, outcomes, and timelines. Influence and coach a distributed team of engineers.
- Performance analysis of ML workloads such as Gemini on future TPU systems using simulation, evaluate performance/cost trade-offs of hardware features and software optimization techniques and drive the next generation TPU Chip and system architecture.
- Work with teams across the full ML Hardware/Software stack, ML models, compilers, ML systems, TPU accelerators, to develop a thorough understanding of the performance/cost trade-offs within a vast ML system design space.
- Help develop a ML accelerator platform performance estimation methodology, enabling unified performance/TCO projection of future ML systems across Google (C++ and Python development).
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Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.
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