Senior Staff Software Engineer

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 8 years of experience in software development.
  
• 5 years of experience with one or more general purpose programming languages including but not limited to: Java, C/C++, Python, Objective C, JavaScript, or Go.
• 5 years of experience with design and architecture, and testing/launching software products.
• Experience in one or more of the following: test automation, refactoring code, test-driven development, build infrastructure, optimizing software, debugging, building tools and testing frameworks.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 8 years of experience with data structures/algorithms.
• 5 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.

In this role, you will be responsible for the journey of a new system from the point of manufacturing to data center delivery. You will do this on projects that push the limits of technologies and provide you with an opportunity to make a broad impact across all of Google's product families. The technologies include systems that support Machine Learning (ML), video transcoding, security, general compute, image processing, and storage.

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 $248,000-$349,000 + 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.

Responsibilities

• Design and manage the development of automation, processes, quality infrastructure, and support new product introduction.
• Ensure efficient turn-up and reliable operation of Google's hyper scale AI/ML/Compute infrastructure, including the timely and effective detection and repair of these systems. Design and use tools to make data driven decisions and strategies to continuously improve the efficacy of the processes, and systems.
• Responsible for the development strategy of new product test solutions, and work with the hardware, software, manufacturing, data center deployment and operation teams to ensure that the end-to-end data center automated diagnosis and repair systems are working efficiently and integrated properly with the signals generated from the test infrastructure and diagnostics.