Staff Software Engineer, GPU Performance, Google Scale
Job Summary
Job Description
- 8+ years software development experience
- 5+ years testing & launching software products
- 5+ years ML design & infrastructure experience
- GPU programming experience (CUDA, OpenCL)
- Experience with compiler optimization
- Experience with OpenXLA, MLIR, Triton
- Knowledge of modern GPU architectures
- Ability to develop performance models
- Experience in algorithms and ML models
- Bonus
- Equity
- Benefits
7 skills required
Job Details
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.
- Knowledge of modern GPU architectures (e.g., NVIDIA, AMD, etc.), memory hierarchies, and performance bottlenecks.
- 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.
In this role, you will support the future of AI and accelerate computing for Google.
The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
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 .
Responsibilities
- Build optimizations that improve benchmarks, but also power Google's most critical products and services, impacting billions of users worldwide and driving 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 optimization techniques through Google’s unparalleled access to the latest generation of GPUs, tools, and build AI accelerators.
- Collaborate with ML, compiler design, and systems architecture teams through internal and external partnerships, as well as open-source projects.
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