Software Engineer, System Test

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 Platforms System Test team provides a full-system perspective on our products, which include computation, storage, accelerators (e.g., TPU and GPU-based supercomputers), and high-speed interconnect solutions. This mission is accomplished by contributing to all phases of product development, including concept, planning, development, and sustaining support. Key artifacts delivered by the team include comprehensive System Test Plans, innovative test software for executing user workloads on TPU/GPU systems, and directions for system qualification and scaling. The team develops automation for execution and creates tools and dashboards to accelerate the triage process and enhance data visualization. As part of the broader Platforms organization, System Test delivers the foundation for Google’s products used both internally and by our Cloud customers.

Our Platforms Infrastructure Engineering team designs and builds the
hardware and software technologies that power all of Google's services. Our computational challenges are complex and unique, enabled by cutting-edge custom hardware designed and made in-house. As a hardware engineer, you will design and build the systems that are the heart of the world's largest and most powerful computing infrastructure. You will see those systems from concept all the way through to high-volume manufacturing. Your work has the potential to shape the machinery that goes into our cutting-edge data centers, affecting millions of Google users.

The US base salary range for this full-time position is $141,000-$202,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Design, develop and execute test plans for TPU and GPU features.
  
• Define and develop automated tests for system level of features and functionality.
  
• Triage the failures reported by automated test suites.
  
• Coordinate with the engineering team to ensure high quality and timely delivery of the project.
  
• Certify software releases for general use.
  

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 2 years of experience with software development or 1 year of experience with an advanced degree in an industry setting.
• 2 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.

Preferred qualifications:

• Master's degree or PhD in Computer Science or a related technical fields.
• 2 years of experience with data structures and algorithms.
• Experience developing accessible technologies.
  
• Experience in machine learning architecture or networking.