Staff Software Engineer, GCS Resource Engineering

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 8 years of experience building and developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage, or hardware architecture.
• 5 years of experience with design and architecture and testing/launching software products.
  
• 3 years of experience in a technical leadership role; overseeing projects.
  
• 3 years of experience working in a matrixed organization involving cross-functional, or cross-business projects.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 3 years of experience designing, optimization-modeling, or debugging large-scale distributed systems and storage architectures such as GCS, Colossus, or Spanner.
• 3 years of experience architecting and optimizing high-volume, programmatic data analysis pipelines using SQL databases or Python-based workflows.
• 3 years of experience applying combinatorial optimization, operations research, or constraint-satisfaction solvers to solve complex, multi-dimensional resource allocation and routing problems.
• Strong cross-functional communication and stakeholder management skills, with experience aligning policy decisions across software development and Site Reliability Engineering (SRE) partners.

About the job

Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. 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 Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. 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.

Google Cloud Storage (GCS) provides a planet-scale object storage service for external GCP customers and internal products like Gmail, Photos, Drive, and YouTube.

The GCS Resource Engineering team ensures predictable capacity, cost, and efficiency, balancing a reliable and scalable service with the lowest unit cost through actionable financial modeling.

This highly complex challenge requires modeling workloads—from multi-billion dollar classic enterprises to hyper-growth AI/ML customers. We optimize allocations across multiple physical and logical dimensions, including bytes, spindles, SSDs, compute (GCUs), and network bandwidth.

As storage evolves to meet high-density compute needs—incorporating next-generation topologies like Megascale, AI Zones, Rapid Cache, and Rapid Buckets—our engineers design state space search, fungibility, and resource isolation frameworks that ensure Google’s storage infrastructure never blocks AI workloads.

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.

Responsibilities

• Define the technical strategy, architecture, and roadmap for GCS capacity planning, resource allocation, and priority-aware scaling systems globally.
• Design and implement complex capacity solver models and resource fungibility algorithms to optimize the utilization of physical and virtual cell compute pools, storage media, and database allocations.
• Architect robust capacity-prioritization frameworks, automated guardrails, and demand-shaping systems to guarantee predictable resource obtainability for critical production serving workloads.
• Build global observability frameworks, real-time metrics, and automated alerts to track resource headroom, identify stockouts, and adapt capacity to physical co