Design and implement complex, multi-host AI training and inferencing solutions on Google Cloud TPUs, focusing on scalability and performance tuning.

Conduct in-depth performance profiling and optimization of customer models and data pipelines specifically for the TPU architecture, identifying and resolving bottlenecks.

Advise customers on best practices for integrating their ML operations workflows with the Google Cloud AI platform ecosystem for seamless TPU utilization.

Bachelor's degree or equivalent practical experience.

10 years of experience in developing and deploying models using deep learning frameworks (e.g., Tensor Flow, Py Torch, or JAX) specifically on TPU hardware.

Experience in networking principles, including concepts like collective communication, inter-chip interconnects, and their impact on distributed AI training.