Shape the future of AI/ML infrastructure at Google. This role focuses on performance engineering within the Core ML organization, driving ML excellence across all Google products. You will contribute to cutting-edge ML tooling and infrastructure that enables high-impact product launches.

As part of Google Cloud, you will help accelerate digital transformation for organizations worldwide. We provide enterprise-grade solutions powered by Google's advanced technology, empowering developers to build more sustainably and solve critical business challenges.

This position offers the opportunity to work on diverse projects, switch teams as needed, and develop versatile leadership qualities while tackling full-stack challenges.

Collaborate with colleagues and stakeholders through code and design reviews, ensuring adherence to best practices for style, code submission, accuracy, testability, and efficiency.

Contribute to and update documentation and educational materials, adapting them based on product updates and user feedback.

Address and resolve product or system issues by analyzing root causes and their impact on hardware, network, or service operations. Implement solutions in specialized ML areas, leveraging ML infrastructure, and contributing to model optimization and data processing.

Influence the evolution of ML Infrastructure at Google, expanding the capabilities of teams with generative and discriminative technologies, thereby making a significant impact on the field. Experience with custom kernel debugging and performance tuning is a considerable asset.

A Bachelor's degree or equivalent practical experience is required.

Possess at least 2 years of software development experience in one or more programming languages, or 1 year of experience with an advanced degree.

Demonstrate 1 year of experience in areas such as Speech/audio technology, reinforcement learning, ML infrastructure, or another ML specialization. Additionally, 1 year of experience with ML infrastructure components like model deployment, evaluation, optimization, data processing, and debugging is necessary.

Preferred qualifications include a Master's degree or PhD in Computer Science or a related technical field, along with 2 years of experience in data structures and algorithms. Experience developing accessible technologies is also a plus.