Job description

Join the Cloud Infrastructure Business Operations team that is transforming the way Apple plans, strategizes and delivers its cloud resources, data centers, and hardware. We are seeking an experienced engineers to drive data and insights on planning the strategy for Apple’s infrastructure. We partner with engineering, finance, procurement and other supply chain teams to align engineering strategies. 10+ years in an engineering role — with a track record of owning and delivering complex initiatives end-to-end. Able to research, architect and drive complex technical solutions, consisting of multiple technologies, cloud services and AI systems — from early prototype to production at scale. Able to write SQL hands-on with AI, understand schema design, and recognize what makes queries expensive. Knows enough to ask data engineers the right questions, not just the broad ones. Experience with compute containerization and orchestration technologies (e.g., Docker, Kubernetes) for pod-based service deployment, operations and lifecycle management. Genuinely curious about AI — actively explores new tools and workflows, brings ideas about what the team should adopt next, and knows how to translate those ideas into concrete asks for platform teams to build or support. Curiosity about GPUs, AI/ML systems and LLM-powered workflows — actively explores new tools and workflows, brings ideas about what the team should adopt next, and knows how to translate those ideas into concrete asks for platform teams to build and support. Ability to define architecture by influencing the cross-functional and software development team, while partnering with business to ensure their strategic vision with software technology is met. Bachelor's Degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience. We value what you can do, not just where you studied. The mindset and ability to work with large data and understand the observability and statistics space specifically. Believes the rules of cloud efficiency are being rewritten — has experience with foundation models, agentic AI, and AI-native workflows, and inspired about how they change the way infrastructure is architected, measured, and optimized. Stays close to the frontier, brings ideas back to the team, and shapes how Apple gets ahead of the shift rather than reacts to it. 5+ years designing, operating, and optimizing infrastructure across public and private cloud environments (AWS, GCP, or equivalent on-premise), with a strong intuition for how architectural decisions translate into cost and performance outcomes at scale. Experience in cloud platforms (AWS, GCP, Azure) — including security, cost optimization, and governance best practices across hybrid and multi-cloud environments. Experience designing and deploying scalable, cloud-native architectures with a strong foundation in distributed systems, reliability, and fault-tolerant design. Hands-on experience with FinOps and cost optimization — using tools like Cloudability or CloudHealth, and applying AI-assisted analysis to surface savings opportunities, model tradeoffs, and drive accountability across engineering teams. Strong analytical foundation — proficient in trending, variance analysis, and forecasting, with the ability to build and communicate AI-augmented models that inform capacity planning and financial strategy. Deep familiarity with Kubernetes — including optimization strategies for resource efficiency, bin packing, autoscaling, and cost-aware scheduling in multi-tenant and dedicated cluster environments. Experience with cloud observability and telemetry tooling (e.g. CloudWatch, Datadog, Honeycomb) — able to connect signals from infrastructure health, performance, and cost into a coherent operational picture. Experience building Finance or FinOps subject areas from the ground up — including unit economics, chargeback models, and the data pipelines and taxonomies that make cost attribution meaningful at scale. Clear, compelling communicator — able to translate complex technical and financial narratives into crisp insights for both engineering and executive audiences, in writing and in the room. Informed perspective on the AI landscape — follows the evolution of foundation models, emerging tooling, and industry adoption patterns closely enough to help shape how Apple prioritizes and invests in AI capabilities across its cloud infrastructure.

Requirements

Must-haves

10+ years in an engineering role — with a track record of owning and delivering complex initiatives end-to-end.
Able to research, architect and drive complex technical solutions, consisting of multiple technologies, cloud services and AI systems — from early prototype to production at scale.
Able to write SQL hands-on with AI, understand schema design, and recognize what makes queries expensive. Knows enough to ask data engineers the right questions, not just the broad ones.
Experience with compute containerization and orchestration technologies (e.g., Docker, Kubernetes) for pod-based service deployment, operations and lifecycle management.
Ability to define architecture by influencing the cross-functional and software development team, while partnering with business to ensure their strategic vision with software technology is met.
Bachelor's Degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience. We value what you can do, not just where you studied.
The mindset and ability to work with large data and understand the observability and statistics space specifically.
Stays close to the frontier, brings ideas back to the team, and shapes how Apple gets ahead of the shift rather than reacts to it.
Experience in cloud platforms (AWS, GCP, Azure) — including security, cost optimization, and governance best practices across hybrid and multi-cloud environments.
Experience designing and deploying scalable, cloud-native architectures with a strong foundation in distributed systems, reliability, and fault-tolerant design.
Strong analytical foundation — proficient in trending, variance analysis, and forecasting, with the ability to build and communicate AI-augmented models that inform capacity planning and financial strategy.
Deep familiarity with Kubernetes — including optimization strategies for resource efficiency, bin packing, autoscaling, and cost-aware scheduling in multi-tenant and dedicated cluster environments.
Experience with cloud observability and telemetry tooling (e.g. CloudWatch, Datadog, Honeycomb) — able to connect signals from infrastructure health, performance, and cost into a coherent operational picture.
Experience building Finance or FinOps subject areas from the ground up — including unit economics, chargeback models, and the data pipelines and taxonomies that make cost attribution meaningful at scale.
Clear, compelling communicator — able to translate complex technical and financial narratives into crisp insights for both engineering and executive audiences, in writing and in the room.

Nice-to-haves

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Keywords to mirror

aisqldockerkubernetesmlllmawsgcpazure

Platform: apple
Effort: medium

Benefits

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Vision