Staff Machine Learning Engineer — Commerce & Growth Intelligence
Confirmed on the employer's own hiring board on Nov 6, 2026. First seen by Alion on Nov 4, 2026. Apple scores A on the Alion truth index.
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.
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.
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.