Machine Learning Manager, Gen AI Frameworks Team

At Apple, we believe machine learning should be deeply integrated, thoughtfully engineered, and responsibly deployed at massive scale. Within the Generative AI Frameworks team, we are building the foundational ML platforms and frameworks that enable teams across the organization to create intelligent, privacy-preserving, and high-impact experiences for millions of users worldwide.

We are looking for an Engineering Manager to lead the development of end-to-end machine learning frameworks that power how models are built, trained, evaluated, and deployed across Apple's services. In this role, you will shape the technical foundations that dozens of product and research teams rely on every day, while building and growing a high-performing engineering team that operates at the intersection of infrastructure, applied ML, and developer experience.

This is an opportunity to influence ML at Apple not through a single product, but by defining the platforms and abstractions that scale innovation across an entire organization.

Description

This role is responsible for leading a team that designs and delivers shared machine learning frameworks used across Apple services to develop, evaluate, and deploy ML models in production. You will partner closely with applied ML teams, infrastructure teams, and product stakeholders to ensure these frameworks are scalable, reliable, and easy to adopt.

You will set technical direction, manage execution, and grow engineers while balancing long-term platform investments with the immediate needs of product teams. Success in this role requires strong systems thinking, deep empathy for ML practitioners, and the ability to translate ambiguous organizational needs into durable technical solutions.

","responsibilities":"Lead and grow an engineering team building core ML frameworks spanning training, evaluation, deployment, and lifecycle management.

Define the technical roadmap for shared ML infrastructure used across SERVICES.

Partner with applied ML, data science, privacy, and product teams to understand requirements and drive adoption.

Drive architectural decisions that prioritize scalability, reliability, privacy, and developer productivity.

Ensure frameworks support best-in-class model evaluation, experimentation, and continuous improvement.

Foster a culture of engineering excellence, operational rigor, and inclusive collaboration.

Preferred Qualifications

Experience building shared ML platforms or frameworks used by multiple teams or organizations.

Deep understanding of the ML lifecycle, including training pipelines, evaluation methodologies, and deployment patterns.

Experience operating ML systems at scale in production environments.

Familiarity with model evaluation, experimentation frameworks, and metrics-driven development.

Experience balancing platform abstractions with flexibility for diverse use cases.

Strong technical leadership skills, including architecture reviews and long-term roadmap ownership.

Demonstrated ability to hire, develop, and retain high-performing engineers.

Experience working in environments with strong privacy, security, or compliance requirements.

Minimum Qualifications

Master's degree in Computer Science or a related field, or equivalent practical experience.