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X In most instances, this position requires in-person interviews as part of the hiring process.
Minimum qualifications: - Bachelor's degree or equivalent practical experience.
- 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
- 2 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- Experience with runtimes and performance tuning.
- Experience in mobile development.
Preferred qualifications: - Master's degree or PhD in Computer Science or related technical fields.
- Experience in 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.
- Passion for innovation and a strong desire to push the boundaries of what's possible with on-device ML.
About the jobLiteRT is Google's on-device AI framework, succeeding TensorFlow Lite (TFLite). It improves the performance, efficiency, and portability of ML models across 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 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.
US: $147000 - $210000 (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).
- Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.
- 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 on-device runtime and kernel implementation.
