Staff Machine Learning Engineer — Commerce & Growth Intelligence

The Staff Machine Learning Engineer position on Apple’s Commerce & Growth Intelligence team focuses on delivering machine learning capabilities for customer-facing products. The work emphasizes model inference and shared ML infrastructure, including integrations that support training, deployment, and fine-tuning workflows.

Role Overview

As a Staff Machine Learning Engineer, you will lead end-to-end engineering efforts across the AI/ML stack. You will collaborate with modeling, infrastructure, and product teams to convert emerging capabilities into production software, with particular attention to inference services, distributed ML workloads, and integration across cloud services and on-device intelligence on Apple devices.

Responsibilities

• Lead engineering projects across the AI/ML stack, coordinating with modeling, infrastructure, and product teams to move new capabilities into production software.
• Lead engineering systems and integrations that support training, deployment, and product usage, while collaborating on model iteration and fine-tuning.
• Build software that connects components, including making models available through reliable inference services and enabling fine-tuning workflows for larger datasets and workloads.
• Shape component integration across model inference, distributed ML workloads, data processing, and cloud service integration as well as on-device intelligence on Apple devices.
• Select and extend open-source infrastructure, design interfaces between components, and address practical constraints related to performance, reliability, compute resources, and deployment environments.
• Resolve architectural tradeoffs and deliver working systems, coordinating with teams through technical leadership and ownership of architectural decisions and delivery.

Required Qualifications

• Experience shipping production ML systems or ML-powered applications, with ownership of significant architecture and implementation decisions.
• Deep expertise in at least one relevant area such as model inference, distributed computing, ML infrastructure, or data systems for ML.
• Strong programming and software engineering skills, including system design, API design, testing, and debugging complex software.
• Practical experience integrating ML models into software systems, including understanding execution and deployment requirements.
• A record of leading technical initiatives across teams, resolving architectural tradeoffs, and delivering working systems.
• Experience investigating performance and reliability issues and driving improvements based on measured results.
• Ability to communicate technical decisions clearly and build alignment across disciplines.
• Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, or Electronic Engineering plus 12+ years of relevant work experience.

Preferred Qualifications

• Distributed ML execution and model serving using Ray, Ray Serve, or comparable technologies.
• Model optimization and deployment using inference runtimes such as ONNX Runtime, Core ML, or comparable systems.
• Distributed training or fine-tuning, including job orchestration, checkpointing, and GPU or other accelerator workloads.
• Large-scale data processing with Spark, Flink, or comparable tools, including dataset preparation for ML.
• Go for backend services and inference APIs.
• Swift for iOS services, daemons, or on-device ML integration.
• Experience with Python for ML workflows, data processing, tooling, or backend services, including applying Python to agent workflows and tool integration using LangGraph, Model Context Protocol (MCP), or related technologies.
• Experience building applications that combine on-device capabilities with cloud-based models and services.
• Master’s degree in Computer Science, Computer Engineering, Information Systems, or Electronic Engineering plus 8+ years of relevant work experience.

Technologies

• Ray, Ray Serve
• ONNX Runtime
• Core ML
• Spark, Flink
• Go, Swift
• Python
• LangGraph
• Model Context Protocol (MCP)

Location

Cupertino, CA (onsite)

Compensation

USD 216,200 - 324,800 per year

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• Range of discounted products and free services
• Reimbursement for certain educational expenses (including tuition) for formal education related to advancing your career at Apple
• Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs (including eligible discretionary restricted stock unit awards and discount on purchasing Apple stock via Employee Stock Purchase Plan)
• Discretionary bonuses or commission payments as well as relocation (if eligible)