Sr. AI Inference Platform Engineer

We are looking for senior engineer to build tooling automation and analysis capabilities that strengthen our AI inference platform. This role will focus on developing sophisticated performance benchmarking systems capacity projection models and data analysis pipelines that directly inform our AI infrastructure teams and capacity planners. Youll work at the intersection of AI systems performance distributed infrastructure and software engineering to help the team make data-driven decisions about scaling and optimizing our inference platform.

At Apple we believe the future of AI is defined not just by models but by the infrastructure that powers them. Our AI inference platform sits at the heart of products and experiences used by hundreds of millions of people worldwide and we are building the systems that ensure it scales reliably efficiently and part of our next-generation datacenter engineering team you will play a critical role in shaping how we understand measure and grow our AI infrastructure. You will design and build the tooling and analysis systems that give our engineers and capacity planners a clear real-time picture of performance across our fleet. Your work will directly influence how we invest in hardware how we detect regressions before they reach production and how we forecast capacity needs months in is a high-impact cross-functional role for an engineer who is energized by complexity thrives on turning raw data into actionable insight and wants to work on problems that matter at massive scale.

Design and build automations to evaluate AI inference performance across hardware generations and tooling to surface performance trends regressions and insights to infrastructure and planning projection and forecasting models to support long-term capacity planning performance and utilization data to identify bottlenecks trends and optimization with AI infrastructure engineers hardware teams and capacity planners to deliver critical data and and enhance performance analysis workflows to increase team velocity and data improve the accuracy coverage and usability of performance measurement and analysis systems.

BS or MS in Computer Science or related technical understanding of AI/ML inference architecture and the performance characteristics of serving systems.n7 or more years of experience with performance and infrastructure engineering in distributed systems.n7 years of experience coding in Python Go C or other programming with automation engineering tooling and data pipelines to support engineering knowledge of GPU/accelerator architecture as it relates to AI statistical knowledge applicable to performance analysis and communication skills and ability to turn data into clear guidance for infrastructure teams and capacity planners.

Experience with performance benchmarking and methodologies for AI/ML inference with capacity planning and forecasting/projection models for large-scale with GPU profiling and observability tools (e.g. Nsight other vendor-specific profilers).nExperience with data visualization and reporting tools/frameworks for surfacing performance trends to with ML serving frameworks and runtimes (e.g. Triton TensorRT-LLM vLLM or similar).nExperience with CI/CD and workflow orchestration tools for building automated performance analysis of cluster schedulers and orchestration platforms (e.g. Kubernetes).nExperience with metrics and logging tools (e.g. Prometheus Grafana Splunk).