Staff Software Engineer, AI/ML, Google Distributed Cloud, Storage

Socket.dev; Kirkland (WA); On-site; USD 207,000 - 300,000; Full time

Benefits offered by this job

Equity

Bonus

Benefits

Job summary

Google’s AI Storage team within Google Distributed Cloud (GDC) designs scalable File and Object storage for AI/ML workloads, delivering ultra-low latency data access for training and inference in edge and on-prem environments.

Engineers collaborate with partners to integrate high-performance storage hardware, tackle I/O bottlenecks, and ensure data security and durability at scale.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience in systems architecture, including building or maintaining distributed systems or large-scale storage architectures.
• 5 years of experience in systems programming (e.g., C++, Go, or Rust).

Responsibilities

• Design and develop scalable, distributed File and Object storage solutions that serve as the critical foundational backbone for complex AI/ML workloads within the GDC environment.
• Engineer advanced solutions optimized for massive throughput, specifically enabling high-frequency model checkpointing and the ultra-low latency data access required for AI training and inference.
• Drive technical execution with external partners to seamlessly integrate industry-leading storage hardware with Google’s distributed software ecosystem.
• Take full life-cycle responsibility for core storage services, ensuring security, data durability, and high availability across disconnected edge and on-premises data center environments.
• Partner closely with AI infrastructure, compute, and networking teams to architect system-wide improvements and deliver a unified cloud-anywhere experience.

Skills

Software development

ML design & infra

Distributed systems

Systems programming

Education

Bachelor’s degree or equivalent practical experience

Master’s or PhD in Engineering/CS/Related field

Job description

MINIMUM QUALIFICATIONS

• Bachelor’s degree or equivalent practical experience.


• 8 years of experience in software development.


• 5 years of experience with ML design and ML infrastructure (e.g., model
  deployment, model evaluation, data processing, debugging, fine tuning).


• 5 years of experience in systems architecture, including building or
  maintaining distributed systems or large-scale storage architectures.


• 5 years of experience in systems programming (e.g., C++, Go, or Rust).

PREFERRED QUALIFICATIONS

• Master’s degree or PhD in Engineering, Computer Science, or a related
  technical field.


• 8 years of experience with data structures and algorithms.


• 3 years of experience in a technical leadership role leading project teams
  and setting technical direction.


• 3 years of experience working in a complex, matrixed organization involving
  cross-functional, or cross-business projects.

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.

The AI Storage team within Google Distributed Cloud (GDC) builds the foundational data layer that powers next-generation machine learning and generative AI workloads at the edge and in air-gapped environments. AI models require massive throughput and ultra-low latency; our team tackles the complex challenge of delivering high-performance, massively scalable storage systems directly to customer data centers. We focus on optimizing the data pipeline to keep GPUs and accelerators fully saturated, ensuring that enterprise and public-sector customers can run advanced AI on their most sensitive data without compromising on sovereignty, security, or speed.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google
[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES

• Design and develop scalable, distributed File and Object storage solutions that serve as the critical foundational backbone for complex AI/ML workloads within the GDC environment.


• Engineer advanced solutions optimized for massive throughput, specifically enabling high-frequency model checkpointing and the ultra-low latency data access required for AI training and inference.


• Drive technical execution with external partners (e.g., VAST Data) to seamlessly integrate industry-leading, high-performance storage hardware with Google’s distributed software ecosystem.


• Take full life-cycle responsibility for core storage services, ensuring uncompromising security, data durability, and high availability across disconnected edge and on-premises data center environments.


• Partner closely with AI infrastructure, compute, and networking teams to architect system-wide improvements, eliminate Input/Output bottlenecks, and deliver a unified "cloud-anywhere" experience.