Staff Machine Learning Engineer — Commerce & Growth Intelligence

Apple•Cupertino, CA

About The Position

The Commerce & Growth Intelligence team is hiring a Staff Machine Learning Engineer to build systems that bring machine learning capabilities into customer-facing products. The work spans model inference, distributed ML workloads, data processing, cloud-service integration, and on-device intelligence on Apple devices. The primary focus is model inference and shared ML infrastructure, with opportunities to contribute to training, fine-tuning, data processing, and device-to-cloud integration. This is a hands-on technical leadership role involving architectural ownership and delivery across teams. The engineer will lead projects across the AI/ML stack and work with modeling, infrastructure, and product teams to turn emerging capabilities into production software. Modeling partners lead model architecture and quality evaluation, while this role leads the engineering systems and integrations supporting training, deployment, and product use, in collaboration on model iteration and fine-tuning. The work includes selecting and extending open-source infrastructure, designing interfaces between components, and addressing performance, reliability, compute-resource, and deployment-environment constraints.

Requirements

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.
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 and build alignment across disciplines.
Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, or Electronic Engineering and 12+ years of relevant work experience.

Nice To Haves

Experience with distributed ML execution and model serving using Ray, Ray Serve, or comparable technologies.
Experience with model optimization and deployment using inference runtimes such as ONNX Runtime, Core ML, or comparable systems.
Experience with distributed training or fine-tuning, including job orchestration, checkpointing, and GPU or other accelerator workloads.
Experience with large-scale data processing using Spark, Flink, or comparable tools, including dataset preparation for ML.
Go experience for backend services and inference APIs.
Swift experience for iOS services, daemons, or on-device ML integration.
Python experience 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.
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 and 8+ years of relevant work experience.

Responsibilities

Lead engineering projects across the AI/ML stack, working with modeling, infrastructure, and product teams to deliver production software.
Own significant architectural decisions and implementation for ML systems and integrations.
Build model inference systems and shared ML infrastructure.
Develop engineering systems and integrations that support model training, deployment, and product use.
Collaborate on model iteration and fine-tuning.
Choose and extend open-source infrastructure and design interfaces between system components.
Resolve practical constraints involving performance, reliability, compute resources, and deployment environments.
Investigate performance and reliability problems and drive improvements based on measured results.
Lead technical initiatives across teams, resolve architectural tradeoffs, and deliver working systems.
Communicate technical decisions clearly and build alignment across disciplines.