info_outline XNote: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; New York, NY, USA. Minimum qualifications: Bachelor’s degree or equivalent practical experience. 5 years of experience with software development in one or more programming languages. 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging). 2 years of experience developing compilers. Relevant work experience in mobile development. Preferred qualifications: Experience leading and delivering successful ML projects focused on on-device deployment (Android, iOS, web browsers, or embedded devices). Experience in ML frameworks (e.g., PyTorch, JAX, TensorFlow). Experience with on-device ML SDKs/tooling (e.g., TensorFlow Lite, ExecuTorch, Core ML, SNPE/QNN). Strong understanding of Generative AI model architectures and their optimization for on-device execution. Excellent communication and collaboration skills. Passion for innovation and a strong desire to push the boundaries of what's possible with on-device ML. 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. LiteRT is Google’s next generation on-device AI framework, succeeding TensorFlow Lite (TFLite). It is designed to maximize the performance, efficiency, and portability of ML models on a wide array of edge devices, from mobile phones to embedded systems. LiteRT significantly upgrades GPU acceleration and introduces native NPU acceleration, while maintaining and enhancing the robust CPU performance inherited from TFLite. LiteRT enables developers and Google products to deploy AI across mobile, web, desktop, and embedded. Our team focuses on building cross-platform infrastructure aligned with Google's business needs, serving top Google products (Android, Chrome, Photos, Meet, Youtube, etc), third-party developers, and specialized Pixel solutions. Our goal is to provide on-device AI infrastructure with exceptional performance, enabling framework and device flexibility at scale. 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: $174000 - $253000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency). Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field. Develop LiteRT, Google's on-device AI framework for first- and third-party, enabling SOTA hardware acceleration and use cases on edge platforms. Enable on-device deployment of key models, such as Gemini Nano and Gemma, across various accelerators (GPU/Pixel TPU/NPUs/CPU) on Android, Chrome, iOS, desktop, and more. Improve performance of on-device model inference via optimizations in the model representation, on-device runtime and kernel implementation.