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.

While known for pioneering work with TPUs, GPUs are an equally vital and rapidly expanding frontier within Google's machine learning infrastructure. 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 generation GPU architectures via strategic partnerships. In recognition of hardware as a strength, Google’s Core ML organization is invested in growing a powerhouse team of GPU experts.

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 $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

• Build improvements for the latest generation of Graphics Processing Unit (GPUs) that power Google’s most critical products and services, impacting users worldwide.

• Identify performance bottlenecks and drive improvements across the breadth and depth of Google’s GPU software stack from ML compiler cost model design (OpenXLA, Triton, MLIR) to optimizing performance GPU kernels (Pallas Mosaic, Cu Te) to cross node model serving configurations (e.g., disaggregated serving, paged attention).

• Influence the technical direction of the GPU software ecosystem at Google by collaborating with Modeling, Accelerated Linear Algebra(XLA): GPU, Deepmind and Performance Tooling teams. 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 in low-level GPU programming (e.g., CUDA, Triton, CUTLASS, etc.) and performance engineering techniques.

• Experience in modern GPU architectures (NVIDIA, AMD, or other AI accelerators), memory hierarchies, and performance bottlenecks.

Preferred qualifications

• Master's degree or PhD degree in Computer Science or related technical fields.

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

• Experience with compiler improvement, 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.