Machine Learning Engineer Platform - iCloud Mail Intelligence
Apple
Software Engineering, Data Science
San Diego, CA, USA
USD 201,300-302,200 / year + Equity
Posted on May 20, 2026
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.
As a machine learning engineer, you will be focusing on: Leveraging existing AI/ML infrastructure, build new platform services and be responsible for building an end to end machine learning based product solution for improving iCloud Mail experiences. Working with large volumes of data; extracting and manipulating large datasets using tools such as Spark SQL, command line and scripting languages. Collect ongoing qualitative and quantitative feedback from the user population and iterate based on the findings. Building high-performance, scalable and extensible REST based services for enhancing Mail consumer experience. Design database schemas, write queries, and optimize database performance. Consider joining a small team writing the software which provides mail services to iCloud customers. We are looking for a very capable engineer who has a strong background in building high-performance, scalable and extensible systems using big data technologies. In addition to crafting efficient, testable, easy-to-maintain code, you recognize the importance of writing functional specifications and design documents. Quality is number one in your mind, and you flourish with building comprehensive unit and end-to-end tests, not only for features you build but also for existing features that need more testing. In this highly transparent position, the successful candidate will enhance existing mail systems while collaborating with multi-functional engineering teams, and also implement new customized mail experiences, in addition to preventing abuse of the system.
- Leverage existing Apple AI/ML infrastructure and build new platform services that
- standardize and accelerate ML feature development across Mail, Calendar, and Contacts
- Design, develop, and deploy end-to-end machine learning applications and models that
- improve the iCloud experience
- Work with large volumes of data; extract and manipulate large datasets using tools such as
- Spark, SQL, command line, and scripting languages
- Build high-performance, scalable, and extensible services for delivering ML models and
- features into production
- Establish and apply standards for evaluation, testing, and model observability
- Collect ongoing qualitative and quantitative feedback from the user population and iterate
- based on the findings
- Partner with multi-functional engineering teams to enhance existing systems and implement
- new ML-driven experiences across Mail, Calendar, and Contacts
- 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
- 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