Senior Software Engineer, Cloud AI/ML Infrastructure
Indexed description
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 testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- Master's degree or PhD in Computer Science or related technical field.
- Experience with Generative AI, Large Language Models (LLM), or machine learning infrastructure, including model deployment, performance optimization, profiling, and debugging.
- Experience with distributed computing leveraging GPUs or TPUs.
- Ability to scope and solve ambiguous problems, grow in a dynamic, fast-paced environment where AI technologies are continuously advancing.
- Ability to demonstrate proven technical leadership by aligning team objectives and timelines with those of multiple adjacent teams.
- Ability to collaborate effectively with cross-functional and cross-regional teams.
The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving team behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Responsibilities
- Optimize performance across the AI infrastructure technical stack through in-depth performance profiling, debugging, and troubleshooting of AI/ML training and inference workloads.
- Develop tools and software for our AI/ML Infrastructure to deliver end-to-end developer experience.
- Collaborate with cross-functional, cross-regional teams to ensure AI/ML infrastructure delivers exceptional value and drives success for customers.
- Identify and resolve performance bottlenecks to maintain infrastructure that operates at peak capacity.
- Shape the future of AI/ML infrastructure by identifying gaps in the existing products and recommending enhancements.
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