Machine Learning Engineer iCloud Anti-Abuse

Apples iCloud Anti-Abuse team protects hundreds of millions of users from spam phishing and malicious content across Mail Calendar and Contacts. nnWe 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.n

This role sits at the intersection of machine learning and distributed systems engineering. You will play a foundational role in building the teams 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 closingnthe feedback loop. The systems you build will run at massive scale across Apples infrastructure. nnSuccess in this role means writing production-quality code reasoning about distributed system tradeoffs and iterating quickly on model performance. nThis is a high-impact role your work will directly determine whether abuse reaches iCloud users or gets stopped.n

nOwn the end-to-end ML lifecycle for abuse detection across Mail Calendar and Contacts: data pipelines feature engineering model training deployment and monitoring n nBuild and maintain ML infrastructure that operates reliably at iCloud scale with low-latency high-availability requirementsnnDevelop techniques to identify and score abusive actors and patterns at scale n nAnalyze model performance identify failure modes and drive continuous improvementnnPartner with backend engineers and cross-functional teams in trust and safety operations and product n nnn

3 years of hands-on machine learning engineering experience including training and deploying models in production n nStrong programming skills in one or more production languages (e.g. Java Scala Kotlin Go Python) n nExperience building and operating ML pipelines: data processing feature engineering training serving and monitoringnnSolid foundation in distributed systems you can reason about scalability fault tolerance and latency tradeoffs n nFamiliarity with classification ranking or anomaly detection techniquesn nAbility to drive projects independently from problem definition to production n nBS in Computer Science Machine Learning or a related technical field or equivalent practical experiencennn

5 years of ML engineering experience (or equivalent depth) with models running at scale in productionn nExperience with abuse detection fraud prevention content filtering or trust and safety systemsn nExpertise in NLP or text classification applied to email messaging or similar domains n nExperience with streaming/real-time ML inference in addition to batch processing n n Familiarity with techniques for scoring ranking or classifying actors and behaviors at scalenn Understanding of privacy-preserving ML techniques and responsible data handling n n Experience with email protocols (SMTP IMAP) or messaging infrastructure n nMS/PhD in Computer Science Machine Learning or a related technical field or equivalent practical experience n