Software Engineer, PhD, Early Career Campus, Machine Learning, Systems and Cloud AI, GPU Performance, 2025 Start

Google Cloud is seeking PhD Software Engineers to join their Machine Learning, Systems and Cloud AI (MSCA) team, focusing on GPU performance optimization and machine learning infrastructure. This role offers an opportunity to work on cutting-edge AI/ML solutions that impact billions of users worldwide.

The position involves working with Google's extensive ML and AI technology stack, including both TPUs and GPUs. You'll be part of shaping the future of AI and accelerated computing, working on transformative solutions that go beyond incremental improvements. The role combines research expertise with practical engineering, allowing you to apply your PhD knowledge to real-world problems at massive scale.

As a Software Engineer in this team, you'll collaborate on critical projects within MSCA, with flexibility to switch teams based on your interests. The role demands creativity, versatility, and leadership qualities as you work on challenges across the full technology stack. You'll be joining an engineering community that includes thousands of PhDs who bring their deep knowledge to enhance Google's systems and products.

Key focus areas include:

• GPU programming and optimization for machine learning workloads
• Development of next-generation AI infrastructure
• Performance optimization and benchmarking
• System architecture and compiler work
• Large-scale distributed computing solutions

The position offers competitive compensation ($141,000-$202,000 + bonus + equity + benefits) and the opportunity to work in major tech hubs including Sunnyvale, Kirkland, New York, or Seattle. You'll be part of Google Cloud's mission to accelerate digital transformation across industries while working with cutting-edge technology and tools.

This role is perfect for PhD graduates who want to apply their research expertise in areas such as compilers, GPU programming, machine learning architecture, or systems optimization to real-world problems at scale. You'll have the chance to influence the future of AI infrastructure while working with some of the most advanced computing systems in the industry.