Machine Learning Engineer - iCloud Anti-Abuse

Apple's iCloud Anti-Abuse team protects hundreds of millions of users from spam, phishing, and malicious content across Mail, Calendar, and Contacts.

We are looking for an ML engineer who can build and ship models in production distributed systems. You will design, train, and deploy ML models that operate at iCloud scale, working across the full lifecycle from data pipelines to real-time inference. You will partner with backend engineers and cross-functional teams in trust and safety, operations, and product to deliver measurable improvements in user protection., This role sits at the intersection of machine learning and distributed systems engineering. You will play a foundational role in building the team's ML capabilities - owning ML-driven abuse detection: building features from high-volume data streams, training and evaluating classification and ranking models, deploying them into low-latency serving infrastructure, and closing

the feedback loop. The systems you build will run at massive scale across Apple's infrastructure.

Success in this role means writing production-quality code, reasoning about distributed system tradeoffs, and iterating quickly on model performance.

This is a high-impact role - your work will directly determine whether abuse reaches iCloud users or gets stopped.

3+ years of hands-on machine learning engineering experience, including training and deploying models in production

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

Experience building and operating ML pipelines: data processing, feature engineering, training, serving, and monitoring

Solid foundation in distributed systems - you can reason about scalability, fault tolerance, and latency tradeoffs

Familiarity with classification, ranking, or anomaly detection techniques

Ability to drive projects independently from problem definition to production

BS in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience

Preferred Qualifications

5+ years of ML engineering experience (or equivalent depth) with models running at scale in production

Experience with abuse detection, fraud prevention, content filtering, or trust and safety systems

Expertise in NLP or text classification applied to email, messaging, or similar domains

Experience with streaming/real-time ML inference in addition to batch processing

Familiarity with techniques for scoring, ranking, or classifying actors and behaviors at scale

Understanding of privacy-preserving ML techniques and responsible data handling

Experience with email protocols (SMTP, IMAP) or messaging infrastructure

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

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $139,500 and $258,100, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.