Join Google's pioneering software engineering team to shape the future of AI/ML Infrastructure. This role focuses on performance engineering, contributing to the next generation of machine learning tools and hardware that empower Google's vast product ecosystem. You will be instrumental in driving ML excellence and innovation across the company, impacting billions of users worldwide.

As part of the Core ML organization, you will work on critical projects that push the boundaries of what's possible with AI. You’ll have the opportunity to switch teams and projects, growing with our fast-paced business and tackling diverse challenges. We seek versatile engineers with leadership qualities, eager to embrace new problems across the full stack.

Collaborate with colleagues and stakeholders through rigorous design and code reviews, ensuring adherence to best practices in areas like style guidelines, code submission, accuracy, testability, and efficiency.

Contribute to and enhance existing 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 and quality.

Implement innovative solutions within specialized ML domains, leveraging ML infrastructure to drive model optimization and data processing advancements.

Influence the trajectory of ML Infrastructure at Google, advancing generative and discriminative technologies to enable teams to achieve unprecedented results and leave a lasting mark on the field.

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. Alternatively, 1 year of experience is sufficient with an advanced degree.

Demonstrate 1 year of experience in at least one of the following: Speech/audio technologies, reinforcement learning, or ML infrastructure.

Acquire 1 year of experience specifically with ML infrastructure, covering aspects like model deployment, evaluation, optimization, data processing, and debugging.