Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 2 years of experience with software development in one or more programming languages.
- 2 years of experience with performance, large-scale systems data analysis, visualization tools, or debugging.
- 2 years of experience with computer architecture, performance analysis, and performance modeling.
Preferred qualifications:- Master's degree or PhD in Computer Science or related technical fields.
- 2 years of experience with data structures or algorithms.
- Experience developing accessible technologies.
About the jobWe are the Core ML Frameworks team, responsible for large parts of Google's production ML stack. We collaborate closely with Google DeepMind and other teams across Alphabet to build solutions that power the future of AI, both within the company and across the industry via GCP. Join Core ML and make a significant impact on Alphabet's vast ML infrastructure, addressing technical challenges that directly impact the performance, efficiency and scalability of AI across Google.
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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $211000 (USD) 15% bonus target bonus equity benefits
Learn more about benefits at Google .
Responsibilities - Contribute to the development and maintenance of Tokamax, a unified open-source kernel library, creating a home for high-quality, well-tested, easy-to-use, and performant kernels available to both internal and external users.
- Build infrastructure and tooling for kernel development, including bench-marking suites, auto-tuning frameworks, performance analysis tools, debugging tools, and continuous integration pipelines to ensure the correctness and performance of custom kernels across different hardware and model configurations.
- Design, develop, and optimize high-performance custom kernels (using languages like Pallas, Mosaic, and Triton) aiming TPU and GPU architectures for key machine learning operations.
- Investigate and implement custom kernel support for new accelerator hardware generations/features and emerging ML operations.
- Contribute to the documentation and usability of kernel libraries tools and libraries to lower the barrier to entry for researchers and engineers looking to write or leverage custom kernels.
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