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You will be a member of Apple’s organization responsible for driving Generative AI and Data Platforms for Apple’s enterprise. The enterprise data warehouse manages petabytes of data, processing billions of rows daily to provide insights and enable informed business decisions. This team delivers data analytics, machine learning, and data science solutions to Apple business groups. Our team champions enterprise Generative AI by delivering platforms and solutions that transform how we work.
We are looking for an ML Engineer to design, build, and deploy production AI and machine learning solutions that deliver real business impact.
Description
You will build and maintain ML models and pipelines that serve Apple's enterprise functions—spanning generative AI, predictive analytics, and automation. You'll own the technical delivery of ML components within broader solutions, working in a fast-paced environment where shipping matters. You'll work across the full ML lifecycle: data wrangling, experimentation, model development, and production deployment. You will be hands-on writing solid production code, debugging pipelines and iterating based on real-world feedback. You'll collaborate with cross-functional partners and business stakeholders to deliver AI and ML systems that solve real problems.
Responsibilities
Design and deploy production ML/GenAI systems that deliver measurable business impact Build automated ML pipelines: data prep, training, evaluation, deployment, monitoring Develop LLM-based solutions (RAG, agents, prompt engineering) for enterprise use cases Integrate ML systems with Apple's enterprise data platform infrastructure Partner with cross-functional teams and stakeholders to scope and ship
Minimum Qualifications
BS in Computer Science, Machine Learning, or related field or equivalent experience Strong Python engineering skills with production ML experience Hands-on experience with LLMs and generative AI systems (RAG, prompt engineering etc) Familiarity with BI concepts and foundational Generative AI concepts (LLMs, prompt engineering)
Preferred Qualifications
Experience with distributed data systems or large-scale data processing Familiarity with agentic frameworks (LangChain, LangGraph or similar) Experience applying data science techniques such as anomaly detection or forecasting to real business problems
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