Software Engineer, On-Device Machine Learning

Job description

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 state-of-the-art (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 symbol, on-device runtime and kernel implementation. 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. 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 software development kits (SDKs)/tooling (e.g., TensorFlow Lite, ExecuTorch, Core ML, SNPE/QNN). 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.