Staff Machine Learning Engineer — Commerce & Growth Intelligence

Apple•Cupertino, CA

About The Position

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

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 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 with 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

Build inference systems for product workloads, including services and APIs for model execution, request routing, batching, concurrency, and scaling.
Make tradeoffs among latency, throughput, memory use, and compute cost.
Develop shared ML infrastructure by adopting and extending open-source and Apple-developed technologies to create reusable components across models and applications.
Partner with modeling teams to improve distributed training and fine-tuning workflows, including job execution, data loading, checkpointing, failure recovery, and accelerator utilization.
Iterate on and fine-tune foundation models and custom-built models as needed.
Build distributed data-processing workflows to join, filter, deduplicate, and transform large datasets into reproducible inputs for training and batch inference.
Address bottlenecks in data movement and processing.
Design interfaces and implement integrations across inference services, backend applications, and on-device components, collaborating with client engineers on iOS and device-to-cloud workflows.
Translate ambiguous requirements into architecture and implementation plans, align teams on interfaces and ownership, and lead projects from prototyping through production.
Build critical components, diagnose issues across data, model execution, and application boundaries, and mentor engineers through design reviews and implementation.
Lead engineering projects across the AI/ML stack and communicate technical decisions to build alignment across disciplines.