Join Google's innovative team as a Software Engineer III specializing in AI/ML Infrastructure and Performance Engineering. You will contribute to next-generation technologies that power billions of users globally. This role offers the opportunity to work on critical projects at massive scale, spanning areas from information retrieval and distributed computing to AI and natural language processing. We seek versatile engineers eager to tackle full-stack challenges and drive technological advancements.

Our team is a vital part of the Core ML organization, providing essential ML software tools and hardware infrastructure to all Google product areas. We are at the forefront of ML innovation across Google, with our team's contributions often being key drivers for high-impact product launches. In this role, you will be instrumental in building and enhancing Google's cutting-edge Machine Learning infrastructure.

You will collaborate closely with peers and stakeholders, participating in design and code reviews to uphold best practices in areas like style guidelines, code integration, accuracy, testability, and efficiency. Contribute to and refine existing documentation and educational materials, adapting them based on product updates and user feedback. You will also be responsible for triaging product or system issues, debugging, tracking, and resolving them by analyzing the root causes and their impact on hardware, network, or service operations.

Implement robust solutions within specialized ML domains, leverage advanced ML infrastructure, and contribute to optimizing models and data processing pipelines. Furthermore, you will have the opportunity to influence the future direction of ML Infrastructure at Google, pushing the boundaries of what's possible with generative and discriminative technologies, and leaving a significant mark on the field.

A Bachelor's degree or equivalent practical experience is required. We are looking for candidates with at least 2 years of software development experience in one or more programming languages, or 1 year of experience with an advanced degree.

Experience in one or more of the following areas is essential: Speech/audio technology, reinforcement learning, ML infrastructure, or specialization in another ML field (1 year of experience). Additionally, 1 year of experience with ML infrastructure components such as 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, 2 years of experience with data structures and algorithms, and experience developing accessible technologies. A background in custom kernel debugging and performance tuning is considered a strong advantage.