Senior Software Engineer, Machine Learning, Kernal

5 days ago

Sunnyvale, California, United States Google Full time $166,000 - $244,000
Minimum qualifications:
  • Bachelor's degree or equivalent practical experience.
  • 5 years of experience in C++, Python, and modern deep learning toolkits like PyTorch or JAX.
  • 3 years of experience in software development for machine learning model inference or machine learning model training, and 1 year of experience with ML model inference and training optimization on modern GPU/TPU architectures.
Preferred qualifications:
  • Experience in Kernel development for TPU.
  • Experience in low-level ML model optimization and willingness to learn new architectures and tools.
  • Experience in developing and optimizing large-scale foundation models, including Mixture of Experts (MoE), Diffusion, and Multi-modal architectures.
  • Familiarity with models and their development issues.
  • Understanding of latency, memory, compute, and quality tradeoffs as they apply to ML model architectures, and practical experience in making these tradeoffs.
  • Ability to maintain agility and deliver results in a changing environment.
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.

Google Cloud is searching for a highly skilled and motivated engineer to optimize machine learning model performance for our customers and help them achieve maximum model performance for large scale training and inference through tuning and optimization at both software and hardware levels. In this role, you will collaborate closely with customers, write custom kernels, and develop custom solutions to meet their unique model performance requirements. A deep understanding of deep learning frameworks (like PyTorch or JAX), strong coding skills, excellent communication abilities, and a passion for mentoring junior engineers are essential for success in this role.

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 $166,000-$244,000 + 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:
  • Optimize ML model architectures and systems for high performance across multiple TPU platforms, including onboard hardware and simulation environments.
  • Enhance model and system performance for both low-latency inference and large-scale distributed training workloads.
  • Develop post-training algorithms, such as quantization and low-level kernel optimizations, to increase inference speed and reduce memory consumption on modern GPU and TPU architectures.
  • Engineer custom kernels to maximize training efficiency for memory-bound large models and I/O-bound fine-tuning processes.
  • Collaborate with ML infrastructure teams, hardware and simulation departments, and Alphabet's research teams to integrate cross-functional optimizations.

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