9 hours ago
Responsibilities
- Build tooling, automation, benchmarking systems, and analysis capabilities for an AI inference platform.
- Develop capacity projection models and data pipelines that support engineering workflows and infrastructure planning.
- Measure and analyze performance across a large infrastructure fleet and detect regressions before production.
- Provide data-driven guidance to AI infrastructure teams and capacity planners on scaling and optimization.
- Build automated performance-analysis workflows using profiling, observability, visualization, and reporting tools.
Requirements
- Bachelor’s or master’s degree in computer science or a related technical field.
- At least 7 years of experience with performance and infrastructure engineering in distributed systems.
- At least 7 years of coding experience in Python, Go, C++, or other programming languages.
- Solid understanding of AI/ML inference architecture and serving-system performance characteristics.
- Experience with automation engineering, tooling, and data pipelines for engineering workflows.
- Strong knowledge of GPU or accelerator architecture as it relates to AI workloads.
- Practical statistical knowledge for performance analysis and forecasting.
- Excellent communication skills and ability to translate data into clear guidance.
- Preferred experience with AI/ML inference benchmarking, capacity planning, forecasting models, GPU profiling, data visualization, ML serving frameworks, workflow orchestration, Kubernetes, and metrics or logging tools.
Tech Stack
Categories
About Apple
Apple designs and sells consumer electronics, software, and services for consumers and professionals worldwide, including iPhone, Mac, iPad, Apple Watch, and AirPods, plus platforms like iOS/macOS and services such as the App Store, iCloud, Music, and TV+. Its business combines device sales with services and subscriptions and in-house silicon design. Founded in 1976, Apple is headquartered in Cupertino, California, and trades on NASDAQ as AAPL.