ML-Driven Cloud Efficiency Manager

Apple, Inc.; San Diego (CA); On-site; USD 226,000 - 338,000; Full time

Benefits offered by this job

Stock program eligibility

Medical and dental coverage

Retirement benefits

Tuition reimbursement

Job summary

Apple seeks a Senior ML Optimization Engineer in San Diego to drive large-scale optimization of ASE cloud services. You will tackle capacity planning, forecasting, and cost models to improve efficiency while meeting reliability and latency targets.

Collaborating with service owners, SRE, data scientists, and finance, you will implement ML-driven approaches and reusable tooling that reduce waste and lower unit costs across Apple’s vast cloud infrastructure, impacting billions of interactions.

Qualifications

• 7+ years in large-scale cloud services or similar environments.
• 2+ years of engineering management experience.
• Strong background in ML-based modeling, time-series forecasting, and data-driven decisions.
• Excellent analytical and cross-functional communication skills.
• Experience with platform observability for insights and optimization.
• Experience developing cost models for software services.

Responsibilities

• Lead and optimize the design, deployment, and operation of services across ASE.
• Collaborate with service owners, infrastructure/SRE, capacity planners, data scientists, and finance to align with cloud sustainability and resource utilization principles.
• Formulate and solve large-scale optimization problems to allocate capacity, schedule workloads, and minimize cost under reliability and latency constraints, integrating solutions into reusable tooling.
• Directly contribute to improved service performance and resource utilization across ASE, influencing unit costs and customer experience.

Skills

Cloud services

Team management

ML forecasting

Data-driven decisions

Observability tools

Cost optimization

Education

Bachelor’s or Master’s degree in Computer Science or related field

Job description

Apple seeks a Senior ML Optimization Engineer in San Diego to drive large-scale optimization of ASE cloud services. You will tackle capacity planning, forecasting, and cost models to improve efficiency while meeting reliability and latency targets.

Collaborating with service owners, SRE, data scientists, and finance, you will implement ML-driven approaches and reusable tooling that reduce waste and lower unit costs across Apple’s vast cloud infrastructure, impacting billions of interactions.