Senior Software Engineer - Cloud Efficiency

Summary

Apple is seeking a Senior Software Engineer to help engineering teams understand, model, and optimize infrastructure demand across large-scale cloud services. This role combines deep application and systems expertise with capacity management, performance engineering, and infrastructure efficiency. You will partner closely with many engineering teams to develop a deep understanding of their applications, architectures, workloads, and growth patterns; identify the signals that drive infrastructure consumption; and translate those signals into measurable resource demand and capacity requirements. This is a hands-on technical role requiring broad experience across distributed systems, application architecture, observability, cloud infrastructure, and capacity planning. You will help teams make informed infrastructure decisions while ensuring resources are used efficiently without compromising reliability, performance, or scalability.

Minimum Qualifications

5+ years of software engineering experience developing, operating, or architecting large-scale distributed services and infrastructure Strong software engineering skills and proficiency in one or more programming languages such as Java, Python, or Go, with experience designing and implementing production-quality systems, tooling, and automation Ability to understand and reason about complex application architectures spanning multiple technologies, services, dependencies, and infrastructure layers Strong understanding of distributed systems and cloud infrastructure, including compute, storage, networking, data technologies, and containerized environments Experience using observability and telemetry, including metrics, logs, and performance data, to understand system behavior, resource utilization, and operational characteristics Ability to translate complex technical problems and system behavior into scalable engineering solutions Excellent communication skills and demonstrated ability to build effective technical partnerships and drive solutions across engineering organizations Bachelor’s or Master’s degree in Computer Science or equivalent experience

Preferred Qualifications

Experience with infrastructure capacity planning, demand forecasting, and resource management, including connecting application and business demand to infrastructure requirements Experience developing scalable capacity, forecasting, optimization, or resource intelligence solutions for large-scale infrastructure environments Experience with Kubernetes and cloud-native architectures, distributed data systems, traffic management, and large-scale service platforms Understanding of FinOps and infrastructure business operations, including cost management, procurement, data center capacity, cloud billing, and financial planning Experience applying GenAI, LLMs, or other emerging technologies to build intelligent automation, derive insights from complex systems, and improve engineering workflows at scale Experience with performance and efficiency engineering across application and infrastructure layers, including identifying architectural, configuration, and operational optimization opportunities Demonstrated ability to define technical standards and methodologies, communicate complex technical concepts, and influence technical direction across engineering and organizational boundaries

Description

You will embed with engineering teams responsible for complex, large-scale services and become a technical partner in understanding how their systems consume infrastructure. The role requires the ability to navigate complex application ecosystems, understand dependencies and architectural characteristics, and connect application and business-level workload signals to underlying compute, memory, storage, network, and platform resource consumption. You will work with teams to establish meaningful demand models, improve observability into resource utilization, forecast future infrastructure requirements, and identify opportunities to improve efficiency. You will also help develop common methodologies, tooling, and standards that allow these practices to scale across multiple organizations and services. Successful candidates will combine strong systems engineering skills with curiosity, analytical thinking, and the ability to build trusted technical relationships across application, infrastructure, operations, finance, and leadership teams.

Responsibilities

A successful candidate will leverage experience across application and infrastructure domains to improve how teams understand, plan, and optimize their infrastructure resources. Responsibilities include: Partnering closely with other engineering teams to understand complex application architectures, dependencies, workload characteristics, operational requirements, and the underlying infrastructure supporting them Developing a deep understanding of how services consume infrastructure across compute, storage, data, network, observability, and other platform resources, and translating that understanding into measurable resource requirements that evolve with application architecture, workload, and business needs Developing scalable engineering solutions, tooling, and automation to provide visibility into resource consumption, validate capacity requirements, identify capacity risks and efficiency opportunities, and establish safeguards that help engineering teams manage their infrastructure effectively Identifying and driving software, configuration, architectural, and operational improvements that increase overall system efficiency while preserving reliability, performance, and scalability Establishing repeatable methodologies, engineering practices, and standards for resource demand modeling, capacity management, and infrastructure efficiency across diverse applications and workloads Collaborating across application engineering, infrastructure, SRE, capacity planning, finance, business partners, and leadership to align resource requirements and technical solutions, and articulate the business impact and tradeoffs of infrastructure decisions and capacity constraints