Write and test product or system development code.
Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
Develop LiteRT, Google's on-device AI framework for 1P and 3P, 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, and more.
Improve performance of on-device model inference via optimizations in the model conversion, 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 testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
3 years of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
Preferred qualifications
Master's degree or PhD in Computer Science or a related technical field.
5 years of experience with data structures and algorithms.
Experience with on-device ML SDKs/tooling (e.g., TensorFlow Lite, ExecuTorch, Core ML, SNPE/QNN).
Knowledge of ML converters/compilers, and hardware-accelerated ML inference techniques and proficiency in ML frameworks (e.g., PyTorch, JAX, TensorFlow).
Understanding of Generative AI model architectures and their optimization for on-device execution.
Bachelor’s degree or equivalent practical experience.
5 years of experience with software development in one or more programming languages.
3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
3 years of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
