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 part of MSCA, (Machine Learning, Systems and Cloud AI), you will have the opportunity to be a part of an organization that delivers category-defining AI/ML solutions and capabilities, developed and powered by Google Services, Frameworks and Infrastructure, supporting customers around the world. Google's 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 a massive scale. You'll be at the forefront of innovation, developing cutting-edge solutions AI, machine Learning, distributed computing, and more. As a new PhD graduate, your research expertise is invaluable to us. Join the ML Systems and Cloud AI organization and apply your knowledge to real-world problems, that scale to billions of users. Explore a variety of projects, collaborate with diverse teams, and contribute to products that are changing the world. Our engineering teams include thousands of PhDs who bring their deep knowledge and research experience to enhance our systems and products. As a Google PhD Software Engineer, you will work on a specific project critical to MSCA's needs with opportunities to switch teams that follow your interests. We need our engineers to be creative and versatile, display leadership qualities and be enthusiastic to take on new problems as we continue to push technology forward. Google is one of the world's leading suppliers and consumers of ML and AI technology, with decades of experience in designing, deploying, and using Machine learning (ML) software and custom ML hardware infrastructure at massive scale. Learn more about us here !

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

• Lead and collaborate on team projects to carry out design, analysis, and development of advanced Machine Learning (ML) systems across the stack using your research expertise.
• Study, diagnose and resolve complex technical modeling and systems issues by analyzing the sources of the issues and the impact on quality.
• Develop code and review code developed by other developers, and provide feedback to ensure best practices (e.g., style guidelines, accuracy, testability, and efficiency).