AI Software Engineer, Apple Cloud AI Platform

Description du poste:

Apple Cloud AI Platform powers the machine learning, AI, and data systems behind intelligent experiences across Apple's consumer products. We are looking for a Software Engineer who is eager to build the tools, interfaces, and workflows that ML developers and researchers use every day from intuitive front-end experiences to backend services and ML integrations at Apple scale. This is a great opportunity for an early-career engineer who wants to grow across the full stack from building intuitive UIs for internal platforms to integrating ML services and workflows into production systems working at Apple scale.
In this role you will work alongside a team of senior engineers to build and ship features across our AI and Data platform. You will contribute to both internal front-end products that teams use to interact with our platform as well as the backend services and ML integrations that power them. You will be expected to take ownership of features end to end, write clean and tested code, and collaborate closely with teammates, product managers, and partner teams. You will grow quickly by working on real systems at Apple scale, with mentorship and clear paths to increased ownership.
1 or more years of industry experience building production software Proficiency in Python and JavaScript/TypeScript Familiarity with REST APIs and backend service integration Basic understanding of ML concepts, model inference, evaluation, or data pipelines Contributions to open-source projects or experience building internal developer tooling Strong communication skills and ability to work collaboratively in a team environment Curiosity, a growth mindset, and willingness to work across the stack BS/MS in Computer Science, Software Engineering, or a related field with applicable internship or project experience
Experience building product experiences or web applications with React or a comparable modern framework Experience with cloud environments (AWS, GCP, or Azure), Docker, or CI/CD pipelines Familiarity with ML frameworks such as PyTorch, Hugging Face, or LangChain Exposure to LLM application development, RAG, prompt engineering, evaluation pipelines, or agentic workflows Experience with data infrastructure tools such as Spark, Kafka, or SQL-based analytics systems Ability to work independently on scoped features with minimal supervision