Gpu Cost Modeling Engineer Jobs

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1d ago

Data Engineer - Capacity Planning

Apple Cupertino, CA

Cupertino, CA

1d ago

$129k - $225k/yr Full time Apache Airflow Big Data Bigdata

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Job Description

Apple is building analytical capabilities to forecast infrastructure demand and costs, starting with third-party cloud resources and extending over time to Apple-owned systems. In this Data Engineer role focused on capacity planning, you will create pipelines, trusted datasets, and forecasting tools that connect utilization telemetry, workload demand, commitments, and financial data into decision-ready models.

This position is based in Cupertino, CA and is an onsite role. The salary range for this role is USD 129,300 - 225,300 per year.

What you’ll do

Build and maintain data pipelines for infrastructure capacity, utilization, performance, and cost data.
Develop trusted data models for GPU, TPU, CPU, storage, and other infrastructure resources.
Create cost models that compute unit economics such as cost per GPU hour, cost per job, and cost per 1M tokens using measured production utilization.
Integrate workload demand, utilization telemetry, capacity commitments, and financial data into a shared planning framework.
Reconcile model outputs to actuals and implement data-quality controls for missing tags, anomalies, and duplicate records.
Build forecasting and scenario-analysis tools to support capacity, utilization, and pricing decisions before spend is committed.
Identify optimization opportunities such as idle reserved capacity, underutilized clusters, and inefficient workloads, then quantify the associated savings.
Automate recurring capacity planning, forecasting, and reporting workflows.
Partner with engineering teams to understand workload growth, migrations, SLOs, and architecture changes that influence capacity needs.
Collaborate with CIBO, Finance, and Procurement to support cloud commitments, infrastructure investment decisions, and long-range capacity planning.
Communicate insights, risks, and recommendations clearly to technical and business stakeholders.

Minimum requirements

3+ years of experience in Data Engineering, Analytics Engineering, Infrastructure Analytics, or a related field.
Strong SQL skills and experience working with large datasets.
Experience with Python (or another language used for data processing and automation).
Experience building data pipelines, data models, and analytical datasets.
Understanding of ETL/ELT patterns, data quality, and pipeline reliability.
Experience working with cloud billing and usage data from AWS, GCP, or Azure.
Proven ability to build data models that reconcile to a financial source of truth.
Understanding of AI and ML inference workloads and how model serving drives compute cost.
Strong analytical and problem-solving skills.
Ability to work effectively with both technical and non-technical partners.
Bachelor’s degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, Economics, Finance, or a related quantitative field, or equivalent practical experience.

Technologies

SQL, Python
ETL/ELT
AWS, GCP, Azure
Spark, Trino, Airflow, Kafka
Tableau

Benefits

Comprehensive medical and dental coverage
Retirement benefits
Discounted products and free services
Reimbursement for certain educational expenses, including tuition
Discretionary bonuses or commission payments as well as relocation
Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
Discretionary restricted stock unit awards
Purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan
Eligibility requirements and other terms of the applicable plan or program

Preferred qualifications

Experience with infrastructure capacity planning, forecasting, or resource-management data.
Experience working with GPU, TPU, CPU, storage, or cloud infrastructure.
Experience with AWS, GCP, or similar cloud platforms.
Understanding of AI/ML infrastructure and accelerator utilization.
Experience with infrastructure cost, billing, or utilization datasets.
Experience with data-platform technologies such as Spark, Trino, Airflow, Kafka, or similar tools.
Experience with Tableau or other visualization platforms.
Familiarity with infrastructure economics, cloud commitments, or capacity optimization.
Experience partnering with Engineering, Finance, or Procurement on infrastructure planning.

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