Machine Learning Compute Efficiency Lead, Infrastructure & Planning

Apples Platform Acceleration u0026 Compute Efficiency (PACE) is a high-leverage team operating at the critical intersection of our ML organizations underlying compute infrastructure and core platform tooling. Our mission is to empower Apples software engineering teams with efficient scalable compute. By driving out operational friction and optimizing the broader machine learning ecosystem we directly accelerate the pace of development across the foundation models become increasingly central to Apples user experiences maximizing the efficiency of our ML compute is this role you will focus relentlessly on compute efficiency ensuring that Apples models run as fast reliably and cost-effectively as possible. You will tackle massive optimization challenges from maximizing hardware utilization across GPUs TPUs and custom Apple Silicon to shaping workload scheduling and capacity allocation for large model are seeking a Senior Architect with deep expertise in ML infrastructure to act as a linchpin for Apples foundational inference strategy. You will be instrumental in defining establishing and monitoring compute efficiency metrics across the software engineering organization. By partnering closely with model developers and infrastructure providers your work will directly reduce serving costs shape core engineering decisions and enable the highly efficient scalable inference required to power Apple Intelligence for hundreds of millions of users.

- Own and support ML compute management for Apples inference workloads (GPU TPU and custom silicon) to enable large-scale model serving.n- Collaborate closely with Apple Intelligence and ML engineering teams to understand roadmaps and resource pain points to develop and implement resource strategies.n- Optimize Apples ML workloads by driving performance improvements maximizing resource utilization and reducing service costs through deep root cause analysis that shapes both engineering decisions and the end customer experience.n- Architect solutions for large-scale optimization problems including capacity allocation workload scheduling and cost reduction enabling Apples AI-driven experiences.n- Advocate on behalf of Apples ML engineers to bring a consolidated view of ML platform and model inference requirements to Apples internal infrastructure platform providers and 3rd party public cloud providers.

BS in Computer Science Computer Engineering or equivalent practical experiencen7 years in ML infrastructure systems architecture or efficiency/optimization roles at scalenStrong conceptual understanding of foundation model inference/serving at scale and distributed training (data/tensor/pipeline parallelism) GPU/TPU utilization memory hierarchies and cluster schedulingnAI-fluent and capable of quickly adapting to AI workflows and empowermentnProven track record of driving complex cross-org technical initiatives through influence not authoritynStrong analytical skills with experience designing or interpreting utilization analyses capacity models or efficiency metricsnClear written and verbal communication comfortable presenting to VPs and white-boarding with senior ML engineers

MS or PhD in a relevant fieldnDirect experience with foundation model serving inference and training at scalenFamiliarity with PyTorch JAX cluster management (Slurm Kubernetes) or GPU/TPU hardwarenPrior experience in efficiency FinOps or capacity planningnExperience negotiating technical roadmaps with platform or infrastructure teamsnBackground in technical and financial decision-making (TCO modeling cost optimization)