{"id":2200202,"url":"https://alion.io/job/apple-machine-learning-engineer-apple-services-engineering","title":"Machine Learning Engineer, Apple Services Engineering","company":{"id":12,"name":"Apple","domain":"apple.com","url":"https://alion.io/company/apple","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Apple Jobs","truth_index":{"grade":"B","score":75,"open_postings":925,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":49,"computed_at":"2026-10-10T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Seattle, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":211000,"max_usd":408000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":413},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Edge AI","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Apache Kafka","optional":true},{"name":"Hadoop","optional":true},{"name":"LLM","optional":true},{"name":"NumPy","optional":true},{"name":"PyTorch","optional":true},{"name":"Scikit-learn","optional":true},{"name":"SciPy","optional":true},{"name":"Spark","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-09T20:08:42Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-11T23:11:48Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Wonder how Apple's Media Products show relevant search results and recommendations across Apple's media offerings - including App Store, Apple TV, Apple Music, Apple Podcasts, and Apple Books? Come join us! Design, build, and deploy recommendation models that personalize the App Store for billions of users worldwide! See your work touch the lives of billions of Apple users worldwide.\nThe Apple Services Engineering team is one of the most exciting examples of Apple's long-held passion for combining art and technology. We are the people who power the App Store, Apple TV, Apple Music, Apple Podcasts, Games and Apple Fitness+. And we do it on a massive scale, meeting Apple's high expectations with high performance, to deliver a huge variety of entertainment in over 35 languages to more than 150 countries.\nOur researchers and engineers build secure, end-to-end solutions powered by machine learning. Thanks to Apple's unique integration of hardware, software, and services, designers, scientists and engineers here partner to get behind a single unified vision. That vision always includes a deep commitment to strengthening Apple's privacy policy, one of Apple's core values. Although services are a bigger part of Apple's business than ever before, these teams remain small, flexible, and multi-functional, offering greater exposure to the array of opportunities here.\nDescription\nWe are looking for an exceptional Machine Learning Engineer to help us design, build, and ship recommendation models that power personalization across the App Store. With your expertise, we want to develop novel solutions to power personalized experiences across the App Store that enrich the lives of our customers. You will have the incredible opportunity to partner with researchers to see cutting-edge AI models deployed reliably at Apple's truly incredible global scale.\nMinimum Qualifications\nBachelor's and Master's in a quantitative field, including Computer Science, Mathematics, Statistics, Physics, etc.\n3+ years of relevant work experience.\nHands-on experience with production-level recommender systems.\nDeep knowledge of recommendation systems, design patterns and tools, with particular depth in deep-learning architectures and multi-task modeling.\nProven track record of shipping recommendation models to production at scale, with a strong understanding of the constraints of serving billions of users.\nKnowledge of modern recommendation architectures across retrieval and ranking, multi-task learning, sequence and transformer-based models, generative recommenders, and multi-objective optimization.\nProven grasp of the open-source Python ML/AI tech stack, including TensorFlow, PyTorch, 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