Manager, Machine Learning Engineer
Confirmed on the employer's own hiring board on Oct 31, 2026. First seen by Alion on Oct 28, 2026. Apple scores A on the Alion truth index.
The Manufacturing Design team enables the mass creation of impossible products of Apple's entire product line, from iPhone, iPad, and Mac to Apple Watch. A key enabler of this success is a robust set of applications and systems designed to support the product lifecycle from prototype to announcement and beyond.
The applications we build are used daily by the teams managing Apple's supply chain and manufacturing. We collaborate closely with cross-functional partners to architect reliable, 24/7 systems that solve complex challenges in producing the highest quality Apple products.
Description
We are seeking an experienced and motivated manager to lead a specialized team of MLE and MLOps Engineers. The ideal candidate combines technical expertise in AI with strong leadership skills to drive engineering excellence and deliver high-quality ML systems aligned with business objectives. You will be responsible for executing the technical vision for critical AI-driven manufacturing applications at Apple, leading the team responsible for building, deploying, and scaling them. You possess the strategic foresight to anticipate challenges without over-engineering solutions and can articulate clear, simple approaches to complex algorithmic problems.
Minimum Qualifications
8+ years of experience in Software Engineering, Data Science, or Machine Learning, including 3+ years in a leadership role.
Proven track record of leading teams to deliver scalable, high-quality Machine Learning models into production environments.
Experience building and managing technical teams with a mix of algorithmic and infrastructure expertise.
Experience leading effective development processes to deliver high-quality production code.
Bachelor’s degree or higher in Computer Science, Artificial Intelligence, Statistics, or a related field.
Preferred Qualifications
Experience with MLOps frameworks (e.g., Kubeflow, MLflow) and containerization (Docker, Kubernetes).
Experience scaling ML systems with large datasets (Computer Vision or Time-Series).
Experience deploying models to Edge devices (Industrial PCs, Gateways) or mobile devices (CoreML).
Experience with cloud platforms (e.g., AWS) and DevOps practices.
Proficiency in Python, PyTorch/TensorFlow, SQL, and Linux.
Knowledge of Agile/Scrum software development methodologies.
Exposure to Manufacturing, Industrial IoT, or Smart Manufacturing environments is a plus.