{"id":2130445,"url":"https://alion.io/job/apple-applied-ai-engineer","title":"Applied AI Engineer","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":["Cupertino, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":208000,"max_usd":402000,"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":"Embeddings","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Swift","optional":false},{"name":"TypeScript","optional":false},{"name":"AWS","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Azure","optional":true},{"name":"Chroma","optional":true},{"name":"Function Calling","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"OpenAI","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Tool Use","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-10-07T23:26:03Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-11T04:41:33Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Imagine building AI infrastructure at Apple - systems that power how products are made, how engineers think, and ultimately how hundreds of millions of people experience technology every day.\nHere is your opportunity to join an engineering team moving Apple's algorithms programs into the AI era. We are developing AI systems that enhance the way engineers work by incorporating human judgment and integrating with data and tools. Our aim is to seamlessly incorporate AI into our processes.\nWe are looking for a software engineer who thrives at the intersection of data systems, AI tooling, and developer experience. You'll design and build platforms that connect AI to the deep organizational context it needs to be genuinely useful - and put those capabilities directly into the hands of the engineers shipping Apple's next-generation sensing technologies. Some of what you build will power internal workflows; some may evolve into products and tools that reach far beyond our team.\nDescription\nYou will work alongside machine learning scientists, algorithm engineers, hardware teams, designers, and human factors researchers to build the AI-native platforms and data systems that accelerate how Apple ships its next generation of devices. We're a small, high-impact group with an ambitious roadmap - you'll have outsized influence on the architecture and direction of what we build.\nThe algorithms behind Apple's most beloved features span software, hardware, and design - and the AI infrastructure you build will serve all of them. You'll be at the center of a uniquely cross-functional environment where world-class talent in each discipline depends on the platforms you create to move faster and make better decisions. The best ideas here have a way of starting as internal tools and growing into something bigger - you'll help shape that trajectory.\nYou might have built a chatbot that needed to reason over internal docs, designed an agent that orchestrated multi-step tool calls, or created a pipeline that turned unstructured data into searchable knowledge. What matters is that you've gone beyond tutorials - you've shipped AI-powered tools that real users depend on.\nMinimum Qualifications\n3+ years of software engineering experience\nProficiency in Python and at least one of: Swift, TypeScript, or another systems-level language\nExperience designing and building backend services, data pipelines, or platform infrastructure\nHands-on experience building with modern AI/agent frameworks\nStrong software architecture and API design sensibilities - you think in systems, not scripts\nExperience building retrieval pipelines (RAG, embeddings, vector search) or knowledge retrieval systems\nStrong communicator who works effectively across disciplines\nExperience evaluating and iterating on AI system outputs - you've built evaluation pipelines, measured quality, and know that shipping AI means shipping feedback loops\nSharp problem-solving instincts and a bias toward shipping\nBS or MS in Computer Science, Software Engineering, Data Engineering, or equivalent experience.\nPreferred Qualifications\nExperience building macOS or iOS client applications\nBackground in workflow orchestration, experiment tracking, or reproducibility tooling\nExperience designing data models, storage layers, or integration APIs for complex domains\nExperience integrating LLMs with external tools and APIs (tool-use patterns, MCP, function calling) or building custom integrations between LLMs and external systems\nFamiliarity with access-controlled or policy-aware data systems\nExperience with vector databases or embedding pipelines (Pinecone, Chroma, pgvector, or similar)\nTrack record building developer tools, CLIs, or internal platforms that engineers rely on daily\nExperience with cloud AI services (AWS Bedrock, Azure OpenAI, Google Vertex AI) or self-hosted model serving\nContributions to open-source projects in AI/ML infrastructure\nComfort navigating large, established codebases and shipping iteratively within 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