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.