Governance & Operations 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 are seeking a founding Operations Lead with a passion for ML compute lifecycle management to build and own our governance and operations function. This is a ground-up opportunity to develop the roadmap and build a world-class high level operations team. You will partner closely with tools and analytics leads to define the governance systems telemetry frameworks utilization dashboards and analytical models that give PACE and Apples ML leadership a clear continuously updated picture of how compute is being used. Success for this role means zero project slips due to process platform or mis-prioritization. Mastery of this role means establishing and tracking key productivity metrics and proactively solving problems to keep those metrics consistently high. Your work will power core ML analytics both for internal development and inference operations roles involve endless runbooks. This role is for someone who is also an engineer and artist at heart. Your operations work will empower a high-leverage team to drive decisions that have a significant impact on Apples financial results.

- Own the daily operations of the systems you architect. You will design and oversee a scalable hub-and-spoke support model spanning cross-functional tier-1 on-call teams tier-2 team leads and a dedicated tier-3 engineering escalation group that you will build and manage.n- Own and evolve PACEs governance tooling and related systems ensuring that compute resource requests allocations and utilization data are accurately captured to support rapid at-scale analysis.n- Bridge coverage gaps as Apples ML ecosystem expands to new hardware (GPUs TPUs and custom silicon) and workloads (inference on-device) balancing power performance cost and compatibility.n- Partner with the Data u0026 Analytics Lead to maintain the analytical layer building the dashboards reports and automated alerts that surface efficiency opportunities and track infrastructure savings.n- Identify system anomalies and operational bottlenecks that degrade utilization and drive up costs building financial impact models that translate technical metrics into actionable insights for leadership.n- Partner with Apples ML engineering teams delivering data-driven analytics to optimize the foundation models inference workloads and platform tooling that rely on your data for success.n- Design robust governance processes and automated operations engineered specifically to meet Apple-scale ML demands.n- Partner to produce strategic analyses that inform executive decisions on ML compute investment allocation and strategy directly influencing Apples ML growth and feature development.

BS in Computer Science Data Science Computer Engineering or equivalent practical experiencen5 years in a governance/operations role data engineering analytics engineering technical program management or in a large-scale compute or cloud environmentnOrganized process-oriented and comfortable owning operational systems other people depend on dailynStrong cross-functional experience working with capable engineers managers EPMs and leadersnProven experience designing and operating complex systems and processes from the ground upnAI-fluent and capable of quickly adapting to AI workflows and empowermentnDirect experience managing SRE and hierarchical technical support systemsnSQL and experience building analytical dashboards or data products (Tableau Looker Grafana or similar)nExperience designing data models or telemetry schemas for infrastructure capacity or utilization datanAbility to translate raw technical metrics into clear business narratives for both engineers and executives

Experience with Python for data analysis (pandas notebooks) or lightweight pipeline developmentnFamiliarity with ML training infrastructure concepts: GPU utilization training throughput and scheduling efficiency mean even if you have not optimized them directlynPrior experience in FinOps capacity planning cloud cost management or IT governancenExperience building or operating data analytics systemsnBackground in automated alerting or anomaly detection for infrastructure metrics