Support new and exciting Machine Learning (ML) paradigms, such as horizontal scaling for upcoming TPU chips, by making contributions across the end-to-end stack and analysis tools.

Learn and build an intuitive understanding of existing data collection, analysis, and visualization workflows.

Understand model optimization use-cases, drive cross-functional efforts to deliver on chip-profiling requirements, and propose new hardware features.

Collaborate across hardware, driver, runtime, and performance analysis teams and other key stakeholders.

Collect and analyze profile data to provide expert-level visualizations and user-actionable advice.

Bachelor’s degree or equivalent practical experience.

5 years of experience in the machine learning field.

5 years of coding experience in one or more of the following languages: C, C++, Java, or Python.

2 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.

1 year of experience with GPU programming.

Experience with engineering tools and infrastructure.