Google

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; Kirkland, WA, USA; Mountain View, CA, USA; New York, NY, USA.

Minimum qualifications:

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

Preferred qualifications:

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

About the Job

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.

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

The US base salary range for this full-time position is $248,000-$349,000 + 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

• Help architect the future of accelerated computing.
• Build optimizations that improve benchmarks, and power Google's most critical products and services, impacting users and driving significant cloud business.
• Shape the entire GPU software stack through influencing model design, optimizing low-level kernels and compilers (OpenXLA, JAX, Triton), and bridging the gap between model developers and hardware for optimal co-design and performance.
• Manage challenging performance bottlenecks and explore optimization techniques through Google’s access to the latest generation of GPUs, tooling, and experience building AI accelerators.
• Collaborate cross-functionally with machine learning, compiler design, and systems architecture through internal and external partnerships, as well as open-source projects.