Staff Software Engineer, GPU Performance, Google Scale

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
• 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• Experience working with GPUs.

Preferred qualifications:

• Experience in low-level GPU programming (e.g., CUDA, OpenCL, etc.) and performance tuning techniques.
• Experience with compiler optimization, code generation, and runtime systems for GPU architectures (e.g., OpenXLA, MLIR, Triton, etc).
• Experience in algorithms and ML models to leverage GPUs effectively.
• Ability to develop and utilize sophisticated performance models and benchmarks to guide optimization efforts and hardware roadmap decisions.
• Knowledge of modern GPU architectures (e.g., NVIDIA, AMD, etc.), memory hierarchies, and performance bottlenecks.

Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. 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 Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. 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.

Google is known for its pioneering work with TPUs, GPUs are equally vital and rapidly expanding within its machine learning infrastructure. GPUs are indispensable to Google’s diverse and ever-evolving landscape for strategic, pragmatic, and performance-driven reasons ensuring performance for ML models, adapting to diverse ML workloads, achieving results, and influencing next-generation GPU architectures through strategic partnerships

In recognition of hardware diversity as a strength, Google’s Core ML organization is heavily invested in growing a powerhouse team of GPU experts.

In this role, you will shape the future of AI and accelerate computing for Google.

The US base salary range for this full-time position is $189,000-$284,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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.

• Build optimizations that improve benchmarks, but also power Google's most critical products and services, impacting billions of users worldwide and driving significant cloud business growth.
• Shape the entire GPU software stack through influencing model design, optimizing low-level kernels and compilers (e.g., OpenXLA, JAX, Triton, etc.), and bridging the gap between model developers and hardware for optimal co-design and performance.
• Manage performance bottlenecks in tests and explore groundbreaking optimization techniques through Google’s unparalleled access to the latest generation of GPUs, tools, and over a decade of experience in building AI accelerators.
• Collaborate with some of the resourceful minds in ML, compiler design, and systems architecture through internal and external partnerships, as well as open-source projects.