Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 8 years of experience in software development, and with data structures/algorithms.
  
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
  
• 5 years of experience building and 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 Engineering, Computer Science, or a related technical field.
  
• 3 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, and/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.

As part of the Cost-Optimized Systems for Machine Learning (ML), Infrastructure, and Compute (COSMIC) team, you will contribute to building and managing an efficient, cost optimal, large-scale fleet for Alphabet. We are responsible for platforms and resource management of both physical and virtual resources that enable cloud and technical infrastructure for Google.

In this role, you'll enable Machine Learning at-scale for Google internally and for Google Cloud, and help enable Google's AI first strategy.

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 $189,000-$284,000 + 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.

In this role, you'll enable Machine Learning at-scale for Google internally and for Google Cloud, and help enable Google's AI first strategy.

In this role, you'll enable Machine Learning at-scale for Google internally and for Google Cloud, and help enable Google's AI first strategy.

Responsibilities

• Provide technical leadership on high-impact projects.
  
• Own the design, development, and productionization of key features of ML Resource Lifecycle Management. Integrate systems across the Unified Fulfillment Optimization (UFO) stack, including planning and resource management tools.
  
• Understand and meet the requirements of the different product area resource managers and ML Fleet.
  
• Design and develop the ordering functionality for abstract Machine Learning Training units (MLT), supply-demand matching solvers that encode buffer strategy and demand pooling, and MLT capacity delivery.
  
• Design and implement the required capabilities to operate ML on top of abstract resources, cell virtualization, chip abstraction, and throughput SLOs.
  

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