Key skills
PythonJavaC++CAIArtificial IntelligenceMLNatural Language ProcessingGoogle CloudCloud StorageGCSLeadership
About this role
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 2 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.
- 2 years of experience developing test automation in C++, Java or Python.
- 2 years of experience with software development or 1 year of experience with an advanced degree in an industry setting.
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical fields.
- 2 years of experience with data structures and algorithms.
- Experience in developing cloud-based storage systems.
- Experience tuning performance and scale in large-scale distributed systems.
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.As a Software Engineer within Google Cloud Storage (GCS) Resource Engineering, you will make significant, independent technical contributions to our capacity planning, resource allocation, and efficiency platforms. You will design, code, and deploy features for GCS capacity pipelines, delivering end-to-end tasks to ensure Google’s storage infrastructure never blocks AI workloads or scaling demands. You will own medium-to-large features from design to launch, directly supporting high-visibility initiatives that guarantee predictability, zonal isolation, and separation for customers. You will collaborate with senior engineers and partner teams to build advanced capacity solvers, automated guardrails, and resource fungibility models. You will align with GCS, Site Reliability Engineering (SRE), product partners to define technical requirements, leveraging Google’s AI/ML tools to maximize engineering velocity and sustain operational excellence.Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s 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: $147000 - $211000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Design, code, test, and deploy new features and enhancements for the GCS Resource engineering capacity planning pipelines, actively leveraging Google's AI tools and platforms to enhance productivity and innovation throughout the development life-cycle.
- Own medium-to-large-scale features from technical design through to launch and operation.
- Collaborate with fellow engineers, product managers, and partner teams to define project requirements and deliver on the product roadmap.
- Contribute to the monitoring, alerting, and overall production health of the system.
- Develop and maintain fluency with the latest internal AI tools and best practices for software engineering, proactively identify opportunities to apply AI to improve efficiency.