Machine Learning Engineer - Proactive

At Apple, machine learning powers experiences that anticipate what people need before they ask. We're looking for a Senior Machine Learning Engineer to help build the next generation of intelligent search and AI experiences technology that understands user intent, context, and personal information while preserving privacy. In this role, you'll work with large language models, semantic retrieval systems, and ranking models to design, optimize, and deploy relevant, personalized, and context-aware experiences across Apple's ecosystem.

Description

You'll work with semantic retrieval systems, and ranking models to design, optimize, and deploy relevant, personalized, and context-aware experiences. You'll build semantic retrieval, embedding, reranking, and retrieval-augmented generation systems that improve search quality and AI-powered experiences. You'll improve the underlying software infrastructure, optimize solutions for low-latency inference, and adapt large foundation models into smaller, highly capable models that operate efficiently under on-device memory, compute, power, and latency constraints.

Minimum Qualifications

Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
Background in machine learning, deep learning, natural language processing, information retrieval, search, recommender systems, or generative AI.
Experience with semantic retrieval, embedding models, reranking, or retrieval-augmented generation systems.
Programming skills in Python and/or C/C++, with experience building production-quality software using modern machine learning frameworks such as PyTorch, JAX, or TensorFlow.

Preferred Qualifications

Master's or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
Experience building and deploying semantic search, retrieval, or ranking systems at scale in a production environment.
Hands-on experience with retrieval-augmented generation, dense retrieval, or neural reranking pipelines.
Experience adapting or distilling large foundation models into compact, efficient models for on-device deployment.
Familiarity with model optimization techniques such as quantization, pruning, or knowledge distillation.
Experience designing and running offline evaluations and online experiments (A/B testing) to measure search quality, ranking, or model performance.
Strong understanding of query understanding, intent modeling, or personalization in search or recommendation systems.
Experience working across cross-functional teams to ship AI-powered features in consumer products