Cloud Infrastructure and AI Efficiency Engineer

The people here at Apple dont just create productsthey create the kind of wonder thats revolutionized entire industries. We know our ability to continue creating the worlds most innovative products depends on people who represent the variety of the human experience and who inspire us with great thinking. Because the broader the backgrounds and perspectives the bigger the ideas.n

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 Apples infrastructure. We partner with engineering finance procurement and other supply chain teams to align engineering strategies.

Build insights into cloud services: architecture cost utilization at Apple scale and collaborate on efficiency with service teams like iCloud Music Siri or Apple in a hybrid and multi-cloud environment across Apples private cloud services and public cloud providers spanning traditional compute storage AI training clusters and inference serving at scale. nUnlock career growth beyond any single with senior engineering and business leaders to translate complex high-dimensional infrastructure data into clear business metrics for strategic recommendations that drive measurable financial and operational value for the infrastructure build services that see privacy as a fundamental right and protect user data at all a force multiplier for engineering teams. nGo beyond reports and dashboards use AI to deliver proactive contextual insights and recommendations rooted in engineering best practices that helps engineering teams make better architectural decisions faster at every stage of the development lifecycle.

5 years in an engineering role with a track record of owning and delivering complex initiatives to research architect and drive complex technical solutions consisting of multiple technologies cloud services and AI systems from early prototype to production at 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 with compute containerization and orchestration technologies (e.g. Docker Kubernetes) for pod-based service deployment operations and lifecycle 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 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 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.

Bachelors Degree in Computer Science Engineering or a related discipline or equivalent practical experience. We value what you can do not just where you mindset and ability to work with large data and understand the observability and statistics space 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. nStays close to the frontier brings ideas back to the team and shapes how Apple gets ahead of the shift rather than reacts to 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 in cloud platforms (AWS GCP Azure) including security cost optimization and governance best practices across hybrid and multi-cloud environments.n Experience designing and deploying scalable cloud-native architectures with a strong foundation in distributed systems reliability and fault-tolerant -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 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 familiarity with Kubernetes including optimization strategies for resource efficiency bin packing autoscaling and cost-aware scheduling in multi-tenant and dedicated cluster 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 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 compelling communicator able to translate complex technical and financial narratives into crisp insights for both engineering and executive audiences in writing and in the 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