{"id":2132178,"url":"https://alion.io/job/apple-on-device-ml-infrastructure-engineer-orchestration-performance","title":"On-device ML Infrastructure Engineer (Orchestration & Performance)","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":"A","score":94,"open_postings":1125,"ghost_share":0.001,"stale_share":0.036,"repost_share":0.02,"time_to_fill_p50_days":148,"computed_at":"2026-11-12T10:38:37Z"}},"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":["Cupertino, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":225000,"max_usd":500000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"MLIR","optional":true}],"status":"live","first_seen_at":"2026-09-02T18:18:21Z","employer_posted_date":"2026-11-13","last_verified_at":"2026-11-13T23:55:37Z","board_verified":true,"closed_at":null,"days_open":72,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":72},"description":"Imagine being at the forefront of an evolution where cutting-edge AI meets the elegance of Apple silicon. The On-Device Machine Learning team transforms groundbreaking research into practical applications, enabling billions of Apple devices to run powerful AI models locally, privately, and efficiently. We stand at the unique intersection of research, software engineering, hardware engineering, and product development, making Apple the leading destination for machine learning innovation.\nOur team builds the essential infrastructure that enables machine learning at scale on Apple devices. This involves onboarding cutting-edge architectures to embedded systems, developing optimization toolkits for model compression and acceleration, building ML compilers and runtimes for efficient execution, and creating comprehensive benchmarking and debugging toolchains. This infrastructure forms the backbone of Apple’s machine learning workflows across Camera, Siri, Health, Vision, and other core experiences, contributing to the overall Apple Intelligence ecosystem.\nIf you are passionate about the technical challenges of running sophisticated ML models across all devices, from resource-constrained devices to powerful clusters, and eager to directly impact how machine learning operates across the Apple ecosystem, this role presents an exciting opportunity to work on the next generation of intelligent experiences on Apple platforms.\nOur group is looking for an ML Infrastructure Engineer, with a focus on model orchestration and performance. The role entails working closely with model authoring, compiler, and runtime teams to ensure that our framework allows executing models with the best stability and performance.\nDescription\nWe’re building an end-to-end developer experience for machine learning development that leverages Apple’s vertical integration. This allows developers to iterate on model authoring, optimization, transformation, execution, debugging, profiling, and analysis. This role focuses on the core runtime for execution across a wide variety of devices and use cases. We’re seeking a highly motivated software engineer who is creative, talented, and passionate about machine learning, common compiler optimizations, and system software engineering in the fast-paced and dynamic field of machine learning.\nMinimum Qualifications\n3-5 years working on tooling built in Python 3 and C++/Swift\nFamiliarity with common ML model architectures, execution schemes, and operations.\nFamiliarity with PyTorch or related training frameworks.\nPreferred Qualifications\nExperience working on or adjacent to MLIR-based compilers.\nFamiliarity with deploying applications or tooling on Apple platforms.\nFamiliarity with programming paradigms for the GPU, CPU, and Neural Engine.\nFamiliarity with writing kernels for ML model 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