Machine Learning Engineer Platform - iCloud Mail Intelligence

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. 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 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