About the job
Scaling machine learning workloads across thousands of accelerators creates challenges that few engineers ever encounter. In Apple’s Machine Learning Platform Technologies organization, we build the infrastructure that powers large-scale ML training and inference workloads, bringing together expertise in distributed systems, machine learning infrastructure, and high-performance computing.
Responsibilities
Develop and maintain models for various capacity forecasting and TCO optimization initiatives.
Analyze supply and demand trends to advise on hardware selection, financials and procurement decisions.
Evolve telemetry systems to expose efficiency and cost opportunities across multi-tenant and heterogeneous fleets.
Build self-service tools enabling ML teams and infrastructure engineers to balance usability, utilization and costs.
Support the team through reviews and knowledge sharing.
Qualifications
Minimum
Ability to translate complex data into easy-to-understand actionable insights and recommendations.
Experience crafting robust queries over large-scale, multi-source data.
Proficiency in scripting, automation or modeling tools.
Experience on cross-functional projects spanning ML research, infrastructure and finance.
Bachelor’s degree or higher in Engineering, Data Science, Economics or a related quantitative field.
Preferred
Experience developing ML models to surface insights and drive solutions.
Familiarity with accelerator utilization patterns across ML training and inference.
Familiarity with cloud compute, storage, network and services.
Comfortable developing with modern web frameworks and RESTful APIs.