Software Engineer, TPU

Staff Software Engineer, TPU Performance

Google place Sunnyvale, CA, USA

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• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience testing, and launching software products.
• 5 years of experience with performance, large-scale systems data analysis, visualization tools, or debugging.
• 3 years of experience with software design and architecture.
• Experience with ML performance analysis and benchmarking.

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 8 years of experience with data structures and algorithms.
• 3 years of experience in a technical leadership role leading project teams and setting technical direction.
• 3 years of experience working in a matrixed organization involving cross-functional or cross-business projects.
• Experience optimizing for NVIDIA/AMD architectures through low-level programming, performance modeling, and bottlenecks analysis to maximize compute efficiency and memory hierarchy utilization.
• Experience in hardware-aware algorithm design and compiler stacks (e.g., OpenXLA), tailoring large-scale ML models and distributed systems for peak performance across accelerator hardware.

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Google's products handle information at massive scale, extending 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 grows daily.

As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as your career grows. You need to be versatile, display leadership qualities, and be ready to tackle new problems across the full stack.

With your technical expertise, you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

In this role, you will work across JAX and PyTorch to squeeze maximum efficiency out of Google’s production and research workloads, like Gemini and other open-source models.

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability, and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving force behind Google's innovations, empowering the development of cutting‑edge AI models, delivering unparalleled computing power to global services, and providing essential platforms that enable developers to build the future. From software to hardware, our teams shape the future of world‑leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and more.

The US base salary range for this full‑time position is $197,000-$291,000 + bonus + equity + benefits. Salary ranges are determined by role, level, and location. Within the range, individual pay is based on work location and other factors, including job‑related skills, experience, and relevant education or training.

These compensation details reflect the base salary only, not bonus, equity, or benefits. Learn more about benefits at Google.

• Focus on Tensor Processing Unit (TPU) fleet efficiency analysis and performance optimization, while identifying and maintaining Machine Learning (ML) training and serving benchmarks.
• Use benchmarks to identify performance opportunities and drive out‑of‑the‑box performance by improving the compiler, runtime, etc. in collaboration with partner teams.
• Collaborate with Google product teams and researchers to solve performance problems, such as onboarding new ML models and…