Senior Software Engineer, Cloud AI/ML Infrastructure

About the job

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.

Responsibilities

• Optimize end-to-end performance on AI infrastructure across the full technical stack to ensure systems operate at peak capacity.
• Conduct in-depth performance profiling, debugging, and troubleshooting of complex AI/ML training and inference workloads.
• Build internal tools and software for AI/ML infrastructure to provide an exceptional developer experience.
• Partner with cross-functional, cross-regional teams to ensure infrastructure delivers maximum value and drives customer success.
• Deliver technical enablement and support for key cloud customers, driving successful deployment and optimization of AI/ML workloads.

Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with software development in one or more programming languages.
• 3 years of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).

Preferred qualifications:

• Master's degree or PhD in Computer Science or a related technical field.
• Experience with Generative AI, Large Language Models, or Machine Learning infrastructure, including model deployment, profiling, and performance tuning.
• Experience with distributed computing leveraging GPUs or TPUs.
• Experience in technical leadership aligning team objectives and timelines with multiple adjacent teams.
• Experience in customer-facing technical support and enablement for enterprise or cloud customers, driving deployment and optimization of AI/ML workloads.
• Ability to scope and solve undefined problems in an environment where AI technologies are advancing.