Overview

Azure is Microsoft’s central cloud infrastructure hosting both public cloud offerings and a wide range of Microsoft internal cloud-scale services. Cloud computing is a highly competitive and rapidly growing market, and Azure aims to be an industry leader across all relevant aspects of its platform and services. Within Azure, the Azure Compute team serves as the core infrastructure organization responsible for hosting virtual machines (VMs), containers, and other workloads. One of the foundational disciplines in cloud computing is capacity management, which must ensure sufficient capacity across all regions, allocation domains, and hardware infrastructure to meet customer demand while also ensuring efficient provisioning to avoid overspending and impacts to cost of goods sold (COGS) and capital expenditures (CAPEX).

At the scale of Azure Compute, managing the balance between availability and efficiency across the entire fleet is a highly complex challenge, where improvements can prevent customer allocation failures and deliver significant savings. The Azure Compute Capacity and Efficiency (AC2E) team is responsible for managing all aspects of capacity and efficiency across the fleet. The team’s primary responsibility is to deliver a fully automated and highly optimized tracking and management system. This system, which includes the Capacity Management Automation System (CMAS) as a core component, uses advanced algorithms and artificial intelligence (AI) to predict capacity risk and execute appropriate mitigation actions directly within the Azure Compute platform.

As a member of this team, individuals work closely with engineers, program managers, and data scientists across Azure Compute platform teams, as well as partners in capacity planning. Responsibilities include formulating business problems and driving solutions end to end, from design through production, while contributing to strategic decision-making for features that impact capacity and efficiency. The impact of this work is reflected in improvements to the Azure platform, service capacity fulfillment rates, customer satisfaction, and efficiency metrics including reductions in cost of goods sold (COGS). The team is composed of data science and development engineers and collaborates closely with program management partners, applying techniques such as anomaly detection, machine learning, and experimentation methodologies to solve complex real-world problems and deliver innovation at scale.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

• Responsibilities- Design new tools and processes to enable better data modeling, analysis, and experimentation for capacity across Azure.
• Understand platform capacity constraints and work with teams across Azure to improve capacity manageability and efficiency.
• Build models, simulations, scalable and automated analytical systems and data mining frameworks to derive profound insights into the Azure Compute platform and its efficiency and capacity.
• Drive improvements to the product design and architecture, leading to increased customer satisfaction
• Lead and collaborate with experts from across the company to advance capacity management, capacity planning, and efficiency.
• Contribute to the team culture and apply best practices in your day to day work.

Qualifications:

Required Qualifications:

• Bachelor's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or PythonOR equivalent experience

Preferred Qualifications:

• Master's Degree in Computer Science or related technical field AND 3+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or PythonOR Bachelor's Degree in Computer Science or related technical field AND 5+ years technical engineering experience with coding in languages including, but not limited to, Python, PERL, Java, or C#
• OR equivalent experience.
• 2+ years of software development hands-on industry experience working on cloud infrastructure-related problems, with impact on critical product and business decisions.
• Experience with Globally Distributed cloud systems with focus on quality and scalability.
• Experience with working across data science and software development boundary.

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Software Engineering IC3 - The typical base pay range for this role across the U.S. is USD $100,600 - $199,000 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $131,400 - $215,400 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.