Machine Learning Engineer Platform - iCloud Mail Intelligence

Summary

Are you passionate about applying your deep understanding of machine learning technologies
and data platform skills in creative ways? Apple's iCloud Mail Intelligence Platform team is
looking for an excellent Machine Learning Engineer that can continuously innovate on the
iCloud experience across Mail, Calendar, and Contacts.

The team is responsible for building groundbreaking ML infrastructure that supports intelligent experiences for hundreds of millions
of users worldwide.

Description

Consider joining a team that brings intelligent experiences to Mail, Calendar, and Contacts for
millions of iCloud customers. Quality and user privacy are central to everything we build.

We are looking for an experienced ML engineer who has a strong background in building high-
performance, scalable, and extensible systems using big data, machine learning, and artificial
intelligence technologies. You recognize the importance of writing functional specifications and
collaborating on high-level design documents. You craft efficient, well-documented code with
comprehensive unit and end-to-end tests.

The successful candidate will demonstrate an ability to collaborate with multi-functional
engineering teams to expand ML infrastructure capabilities and build ML-driven experiences
across Mail, Calendar, and Contacts. You will leverage existing AI/ML infrastructure and
contribute to building new platform services that accelerate machine learning development.
You will also design, build, and deploy ML models and features that improve the iCloud
experience for millions of users.

Preferred Qualifications

5+ years of ML engineering experience (or equivalent depth) with a track record of technical leadership on large-scale ML systems or ML platforms that standardize workflows across multiple teams

Experience with agent-based architectures, orchestration frameworks, and LLM observability and evaluation tooling

Expertise with LLMs, including fine-tuning, prompt engineering, embeddings, retrieval systems, evaluation, and integration into production systems

Experience deploying models across multiple runtimes (e.g., on-device, server-side)

Understanding of privacy-preserving ML techniques and responsible data handling

Familiarity with email, calendar, or contacts domains, or other communications and productivity systems

MS/PhD in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience

Minimum Qualifications

Strong production experience training, evaluating, and operating ML models with end-to-end ML pipelines: data processing, feature engineering, training, serving, and monitoring

Experience with large-scale distributed systems including data processing, event-driven architectures and both real-time and batch inference

Strong programming skills in one or more production languages (e.g., Python, Java, Scala, Kotlin, Go)

Demonstrated ability to drive projects independently from problem definition to production

Deep understanding of predictive modeling and machine learning algorithms across supervised and unsupervised learning