Staff Machine Learning Engineer - Commerce & Growth Intelligence

You’ll lead engineering projects across the AI/ML stack, working with modeling, infrastructure, and product teams to turn emerging capabilities into production software. Modeling partners lead model architecture and quality evaluation; you’ll lead the engineering systems and integrations that support training, deployment, and product use, while collaborating on model iteration and fine-tuning. You’ll combine depth in a core area with the ability to work across system boundaries. The work also includes choosing and extending open-source infrastructure, designing interfaces between components, and resolving practical constraints involving performance, reliability, compute resources, and deployment environments.

• 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 and understanding their execution and deployment requirements.
• A track record of leading technical initiatives across teams, resolving architectural tradeoffs, and delivering working systems.
• Experience investigating performance and reliability problems and driving improvements based on measured results.
• Ability to communicate technical decisions clearly, build alignment across disciplines.
• Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, or Electronic Engineering and 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; experience applying Python to agent workflows and tool integration using LangGraph, Model Context Protocol (MCP), or related technologies is an additional plus.
• 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 and 8+ years of relevant work experience.

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $216,200 and $324,800, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits