Staff Machine Learning Engineer - Commerce & Growth Intelligence

Summary

The Commerce & Growth Intelligence team is looking for a Staff Machine Learning Engineer to build the systems that bring ML capabilities into customer-facing products. Our work spans model inference, distributed ML workloads, data processing, and integration with cloud services and on device intelligence on Apple devices. You’ll help shape how these components fit together and build the software that connects them—from making a model available through a reliable inference service to enabling a fine-tuning workflow to handle larger datasets and workloads. Your primary focus will be model inference and shared ML infrastructure, with opportunities to extend that work into 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.

Minimum 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 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.

Description

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.

Responsibilities

Build inference systems for product workloads. Develop services and APIs for model execution, including request routing, batching, concurrency, and scaling. Make tradeoffs among latency, throughput, memory use, and compute cost. Develop shared ML infrastructure. Adopt and extend open-source and Apple-developed technologies where appropriate, to build reusable components across models and applications. Scale fine-tuning and training workflows. Partner with modeling teams to improve distributed job execution, data loading, checkpointing, failure recovery, and accelerator utilization. Responsibilities may also include iterating on and fine-tuning foundation models and custom-built models. Build data-processing workflows for ML. Use distributed processing to join, filter, deduplicate, and transform large datasets into reproducible inputs for training and batch inference. Address bottlenecks in data movement and processing. Connect ML systems with product software. Design interfaces and implement integrations across inference services, backend applications, and on-device components, working with client engineers on iOS and device-to-cloud workflows. Set technical direction and deliver it. Turn ambiguous requirements into architecture and implementation plans, align teams on interfaces and ownership, and lead projects from prototyping through production. Raise engineering quality through hands-on leadership. Build critical components, diagnose issues across data, model execution, and application boundaries, and mentor engineers through design reviews and implementation.