Machine Learning Engineer (Applied Machine Learning), AI and Data Platforms (AiDP)

Our Machine Learning Engineers work on building intelligent systems to democratize AI across a wide range of solutions within Apple. You will drive the development and deployment of AI models and systems that directly impact the capabilities and performance of Apple's products and services. You will implement robust, scalable ML infrastructure, including data storage, processing, and model serving components, to support seamless integration of AI/ML models into production environments. You are a creative problem solver with strong ML and engineering skills who will implement automated ML pipelines for data preprocessing, feature engineering, model training, hyper-parameter tuning, and model evaluation, enabling rapid experimentation and iteration., * Design and deploy production ML/GenAI systems that drive measurable business outcomes across Apple's product ecosystem

• Build next-generation infrastructure leveraging distributed systems, hardware acceleration, and optimization techniques
• Partner with cross-functional teams to translate groundbreaking research into user-centric products
• Solve uniquely challenging problems in privacy-preserving ML and efficient inference at scale.
• Champion ML engineering excellence through robust testing, monitoring, and documentation that meets Apple's quality bar

• Bachelor of Science in Machine Learning, Data Science, Computer Science or a related quantitative field or equivalent experience
• Demonstrated experience in Machine Learning engineering with solid experience in Python
• Hands-on experience with LLMs and generative AI systems (e.g. RAG, prompt engineering, evaluation) as well as agentic frameworks
• Experience building enterprise-grade ML pipelines (data prep, distributed training, optimisation, monitoring) in cloud environments (AWS, GCP, Azure) or on-prem infrastructure, * Contributions to major open-source ML frameworks or research communities
• MS in Computer Science, Machine Learning, or a related quantitative field
• Solid grasp of NLP techniques, multimodal AI (text, image, code), and agent workflows.
• Experience with LLM Agentic workflows and framework (Langchain, LangGraph, DSPy, or similar.)
• Experience applying core data science methods such as anomaly detection, forecasting, clustering, and pattern discovery - and translating those insights into impact
• Familiarity with performance optimisation for ML workloads (hardware acceleration, inference tuning)
• Familiarity with designing data pipelines and producing aggregated datasets