Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
  
• 2 years of experience developing infrastructure, distributed systems, or networks, or experience with compute technologies, storage or hardware architecture.
  
• Experience in API design and development.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical fields.
  
• Experience developing accessible technologies.
  
• Experience building AI/ML systems and distributed systems.
  
• Experience in Google Cloud Platform or similar public cloud platforms.
  

Summary

• Bachelor’s degree or equivalent practical experience.
  
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
  
• 2 years of experience developing infrastructure, distributed systems, or networks, or experience with compute technologies, storage or hardware architecture.
  
• Experience in API design and development.

Description

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.

The Cloud Machine Learning Compute Service Platforms and Experience team delivers the service platform for Machine Learning (ML) Computing on Google Cloud including Tensor Processing Units (TPUs). Google’s Cloud TPUs were designed to accelerate machine learning workloads, providing the computational power to train and run machine learning models in a fraction of the time and cost conventionally required.

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.

The US base salary range for this full-time position is $136,000-$200,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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

• Build and improve the accelerator platform where the team onboards first-party machine learning TPUs to Google Cloud Platforms (GCP).
  
• Design and implement features and scale the Cloud TPU GCP API infrastructure to support multiple generations of TPU hardware and products.
  
• Work with software engineers, product managers, technical program managers, and other engineering teams to get high-quality products and features through the software project life-cycle.
  
• Impact various layers of the stack including the Cloud front-end, control plane, data plane, storage, GCP horizontals, as well as system scalability and performance improvements.
  
• Manage project schedules, identify technical risks and communicate them to project stakeholders.
  

Additional_instructions