Software Engineer III, AI/ML Infrastructure, Performance Engineering

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

Our team is part of the Core ML organization which is the central machine learning organization that provides ML software tools and hardware infrastructure to all the Google product areas and is driving ML excellence for Google and the world. Our team is involved in various aspects of AI and infrastructure that are pushing the ML frontier across all of Google. The tooling and engagements provided by our team are often key drivers for several high impact landings.

In this role, you will build and improve the next generation of Machine Learning (ML) Infrastructure at Google.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).

Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.

Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.

Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.

Influence the future of ML Infrastructure at Google, pushing the boundaries of what teams can achieve with generative and discriminative technologies, leaving a lasting impact on the field. Custom kernel debugging and performance tuning background are considered a strong bonus.

Minimum qualifications:

Bachelor’s degree or equivalent practical experience.

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

1 year of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.

1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Preferred qualifications:

Master's degree or PhD in Computer Science or a related technical field.

2 years of experience with data structures and algorithms.

Experience developing accessible technologies.

Bachelor’s degree or equivalent practical experience.

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

1 year of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.

1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).