{"id":1310824,"url":"https://alion.io/job/apple-machine-learning-engineer-proactive","title":"Machine Learning Engineer, Proactive","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":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","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":263000,"max_usd":542000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":618},"experience_years_min":10,"visa_sponsorship":true,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"C++","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"JAX","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Reranking","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"BERT","optional":true},{"name":"Edge AI","optional":true},{"name":"Function Calling","optional":true},{"name":"Gemma","optional":true},{"name":"Human-in-the-Loop","optional":true},{"name":"Llama","optional":true},{"name":"Mistral","optional":true},{"name":"Semantic Search","optional":true},{"name":"Semantic Search","optional":true},{"name":"Tool Use","optional":true}],"status":"live","first_seen_at":"2026-09-26T03:59:35Z","employer_posted_date":null,"last_verified_at":"2026-09-26T03:59:35Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"At Apple, machine learning powers experiences that anticipate what people need before they ask. We're looking for a Machine Learning Engineer to help build the next generation of intelligent search and AI experiences technology that understands user intent, context, and personal information while preserving privacy. In this role, you'll design, train, fine-tune, optimize, and deploy large language models, semantic retrieval systems, and ranking models that power relevant, personalized, and context-aware experiences across Apple's ecosystem.Description\nYou'll design, train, fine-tune, and optimize transformer-based language models and foundation models for efficient on-device deployment, and build semantic retrieval, embedding, reranking, and retrieval-augmented generation systems that improve search quality and AI-powered experiences. You'll develop models for query understanding, intent prediction, personalization, retrieval, and ranking, while researching new approaches to LLM fine-tuning, knowledge distillation, model compression, quantization, and low-latency inference. You'll explore techniques for adapting large foundation models into smaller, highly capable models that can operate efficiently under on-device memory, compute, power, and latency constraints.\nYou'll partner with engineers, researchers, product managers, and designers to bring new AI capabilities from research into production, driving technical strategy and leading projects from early exploration through large-scale deployment. This is an opportunity to explore new applications of foundation models, multimodal AI, agentic retrieval, and personalized intelligence, shaping the next generation of proactive and intelligent user experiences.\nMinimum Qualifications\nBachelor degree in Computer Science, Machine Learning,\nArtificial Intelligence, or a related field.\nBackground in machine learning, deep learning, natural\nlanguage processing, information retrieval, search,\nrecommender systems, or generative AI.\nExperience training, fine-tuning, or deploying transformer-\nbased models and large language models.\nExperience with modern deep learning architectures and\ntechniques, including transformers, embeddings,\nrepresentation learning, and neural ranking.\nProgramming skills in Python and/or C/C++, with experience\nbuilding production-quality software using modern machine\nlearning frameworks such as PyTorch, JAX, or TensorFlow.\nAbility to work onsite in Cupertino, California, in accordance\nwith Apple's applicable work policies.\nPreferred Qualifications\nMaster's or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field.\nExperience optimizing machine learning models for resource-constrained environments, including knowledge distillation, model compression, quantization, and pruning.\nExperience with on-device machine learning or edge AI, or mobile inference frameworks, including optimizing models for latency, memory, compute, and power constraints.\nExperience distilling capabilities from large foundation models into small language models or task-specific models for efficient inference.\nExperience building retrieval-augmented generation, vector search, embedding retrieval, neural reranking, or semantic search systems.\nExperience with query understanding, query rewriting, intent classification, personalized retrieval, learning-to-rank, or recommendation models.\nExperience working with transformer architectures and foundation model families such as BERT, T5, Llama, Gemma, Mistral, or related architectures.\nExperience evaluating language models, designing AI quality metrics, and building automated and human-in-the-loop evaluation pipelines.\nExperience building large-scale production search, recommendation, personalization, or generative AI systems.\nFamiliarity with multimodal foundation models, tool use, agentic AI, or agentic retrieval systems.\nStrong understanding of the tradeoffs among model quality, latency, memory, power consumption, privacy, and reliability for production on-device AI systems.\nAbility to prototype new ideas, conduct rigorous experiments, solve ambiguous technical problems, and translate research advances into production-quality machine learning solutions.","description_format":"text","description_chars":4238,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["Operating Systems","Laptops","Smartwatches & Fitness Trackers","Tablets"],"lifecycle":[{"event":"open","at":"2026-09-26T16:07:58Z"}],"liveness":{"score":99,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.993,"p_room":1,"age_days":2,"expected_fill_days":27,"reasons":["seen:2","urgency","velocity","win:early"],"computed_at":"2026-09-28T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/apple-machine-learning-engineer-proactive","json_url":"https://alion.io/job/apple-machine-learning-engineer-proactive.json","meta":{"generated_at":"2026-09-28T23:08:06Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":702,"day_limit":5000,"remaining_today":4298,"minute_limit":60,"resets_at":"2026-09-29T00:00:00Z"}}}