Join Google as a Software Engineer III, specializing in AI/ML Infrastructure and Performance Engineering. This role is integral to developing cutting-edge technologies that shape how billions of users interact with information. You will contribute to projects at a massive scale, extending beyond web search and encompassing areas like information retrieval, distributed computing, AI, and natural language processing.

As part of the Core ML organization, you will enhance Google's central machine learning infrastructure, providing essential tools and hardware for product areas across the company. Your work will push the boundaries of ML across Google, driving high-impact innovations and significantly contributing to the field.

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

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

Address and resolve product or system issues by triaging, debugging, and tracking problems to their source, analyzing their impact on hardware, network, or service operations.

Implement solutions within specialized ML domains, leveraging ML infrastructure, and contributing to model optimization and data processing efforts.

Influence the future direction of ML Infrastructure at Google, advancing capabilities in generative and discriminative technologies and leaving a lasting impact on the field. Experience with custom kernel debugging and performance tuning is a significant advantage.

A Bachelor's degree or equivalent practical experience is required. Candidates should possess two years of software development experience in at least one programming language, or one year of experience if holding an advanced degree.

One year of experience is needed in one or more of the following areas: Speech/audio technologies, reinforcement learning, ML infrastructure, or a specialization in another ML field. Additionally, one year of experience with ML infrastructure, including model deployment, evaluation, optimization, data processing, and debugging, is essential.

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