Software Engineer, GPU Performance

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.

While known for pioneering work with TPUs, GPUs are an equally vital and rapidly expanding frontier within Google's machine learning infrastructure. Graphics Processing Units (GPUs) are indispensable to Google’s ever-evolving landscape for strategic, pragmatic, and performance-driven reasons ensuring top performance for our ML models, adapting to ML workloads, achieving results, and influencing next-gen GPU architectures via strategic partnerships.

In recognition of hardware as a strength, Google’s Core ML organization is heavily invested in growing a powerhouse team of GPU experts, and we invite you to be at its vanguard. This is your opportunity to move beyond incremental improvements and architect truly transformative solutions, shaping the future of AI and accelerated computing for Google and the world.

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 groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping 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 much more.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $211000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Build optimizations for the latest generation of GPUs that power Google’s most critical products and services, impacting billions of users worldwide.
• Address the most challenging performance bottlenecks through Google’s unparalleled access to the latest generation of GPUs, tooling, and a decade of experience building AI accelerators.
• Drive optimizations across Google’s GPU software stack from ML compiler cost model design to optimizing high performance GPU kernels to cross node model serving configurations.
• Influence the technical direction of the GPU software ecosystem at Google by collaborating with ML, compiler design, and systems architecture.
• Influence the deployment of Google’s GPU fleet by working with various product teams across Google.

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.
• Experience low-level GPU programming (CUDA, Triton, CUTLASS, etc.) and performance engineering techniques.
• Experience with modern GPU architectures (NVIDIA, AMD, or other AI accelerators), memory hierarchies, and performance bottlenecks.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
• 2 years of experience with data structures and algorithms in either an academic or industry setting.
• Experience with compiler optimization, code generation, and runtime systems for GPU architectures (OpenXLA, MLIR, Triton, etc.).
• Understanding of modern Large Language Models (LLMs) and their deployment on AI accelerators.