Software Engineer, Core Data Processing Infrastructure, Conduit

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in C++ programming languages, or 1 year of experience with an advanced degree.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical fields.
• 2 years of experience with data structures or algorithms in either an academic or industry setting.
• Experience architecting and developing large scale distributed systems.
• Experience in code and system health, diagnosis and resolution, and software test engineering.

About the job

The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.

The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.

Responsibilities

• Build highly scalable distributed infrastructure for batch and incremental data processing.
• Leverage AI to improve the velocity, efficiency and reliability.
• Build machine learning infrastructures for Gen AI products.
• Index, monitor and cach petabytes of data efficiently.
• Improve resource efficiency.