At Apple, we're building the next generation of AI systems that power experiences for billions of users worldwide. The AI & Data Platforms (AiDP) team is seeking an ML Engineer to architect and deploy production-scale generative AI systems that balance innovation with Apple's uncompromising standards for privacy, performance, and quality. The successful candidate will own end-to-end ML initiatives, from problem framing and experimentation to production deployment and measurable business impact.
If you're passionate about transforming research into robust ML infrastructure and solving complex engineering challenges at the intersection of GenAI and distributed systems, we want to hear from you.
Description
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.
Minimum Qualifications
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
Preferred Qualifications
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