Software Engineer - PhD

We seek innovative engineers with various expertise, including information retrieval, distributed computing, large-scale system design, networking, data storage, security and AI/ML. Within Google Cloud, the AI and Infrastructure(AI2) organisation is at the forefront of creating groundbreaking AI/ML capabilities. We are responsible for designing and managing the software, hardware, and Machine Learning (ML) systems infrastructure that power essential Google services such as search and YouTube, as well as Google Cloud products. As a PhD Software Engineer within AI2 your specialised research expertise will be instrumental in solving complex, real-world problems on an unprecedented scale. You will engage in innovative projects spanning areas like AI/ML, networking, data storage, security, and distributed systems.

Responsibilities:

• Develop kernel drivers for the various Infrastructure Processing Units (IPUs)/Network Interface Cards (NICs) in our portfolio, and ensure that end-to-end systems built with these devices achieve the scale, reliability, and performance expected of networking, and that such systems interact well within the Google Cloud ecosystem.

Requirements:

• PhD degree in Computer Science, Electrical Engineering, or a related technical field.
• Experience coding in one of the following programming languages, including but not limited to: C, C++, Java or Golang.
• Experience in software development, with a focus on areas such as large-scale distributed systems and networking.
• Experience in algorithms, complexity analysis, and system design.

Preferred qualifications:

• Experience working with SmartNIC technologies.
• Ability to act as a generalist software engineer blending technical execution with program management and process optimisation.
• Ability to lead multiple workstreams with attention to detail.
• Ability to write and review code, ensuring adherence to best practices in style, accuracy, testability, and efficiency.
• Ability to investigate, diagnose, and resolve technical issues by analysing root causes and impact.