Senior Software Engineer, AI​/ML

Senior Staff Software Engineer, AI/ML

_corporate_fare_ Google _place_ Sunnyvale, CA, USA

** Advanced*
* Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain.

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* Minimum qualifications:

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* + 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 with design and architecture; and testing/launching software products.

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Experience with machine learning infrastructure, C++, performance, GPU programming, mobile GPU.

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* Preferred qualifications:

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* + Master's degree or PhD in Engineering, Computer Science, or a related technical field.

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Experience with on-device ML Software Development Kits (SDKs)/tooling (e.g., Tensor Flow Lite, Execu Torch, Core ML, SNPE/QNN).

+ In-depth knowledge of ML converters/compilers and runtimes, and hardware-accelerated ML inference techniques.

+ Strong understanding of generative AI model architectures and their optimization for on-device execution.

+ Proven track record of leading and delivering successful ML projects focused on on-device deployment (Android, iOS, web browsers, or embedded devices).

** 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 AI Edge enables developers and Google products to deploy AI across mobile, web and embedded with with our AI edge stack (ai.google.dev/edge) from low-code APIs to hardware specific acceleration libraries to achieve leading performance and device optionality  team focuses on cross-platform infrastructure and solutions serving all of Google AI's business needs.

Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google's 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: $262000 - $365000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google () .

** Responsibilities*
* + Train task-specific models of in app AI (tiny Gemma/Juno Nano), enabling Gemma model/runtime co-design (quantization, conversion), build industry-leading on-device AI solutions (voice translate, generative image editing).

+ Enable developers to test/evaluate/deploy across devices (Edge Portal /Model Explorer/Developer Device Platform).

+ Develop and guide critical projects in Google's on-device ML infrastructure (e.g., LiteRT, LiteRT-LM).

+ Enable on-device deployment of key models, such as Gemini Nano and Gemma, across various accelerators (GPU /Pixel TPU /NPUs/CPU) on Android, Chrome, and more.

+ Improve performance of on-device model inference via optimizations in the model representation, on-device runtime and kernel implementation.

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