You'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 design, train, fine-tune, optimize, and deploy large language models, semantic retrieval systems, and ranking models that power relevant, personalized, and context-aware experiences across Apple's ecosystem.
Description
You'll design, train, fine-tune, and optimize transformer-based language models and foundation models for efficient on-device deployment, and build semantic retrieval, embedding, reranking, and retrieval-augmented generation systems that improve search quality and AI-powered experiences. You'll develop models for query understanding, intent prediction, personalization, retrieval, and ranking, while researching new approaches to LLM fine-tuning, knowledge distillation, model compression, quantization, and low-latency inference. You'll explore techniques for adapting large foundation models into smaller, highly capable models that can operate efficiently under on-device memory, compute, power, and latency constraints.
You'll partner with engineers, researchers, product managers, and designers to bring new AI capabilities from research into production, driving technical strategy and leading projects from early exploration through large-scale deployment. This is an opportunity to explore new applications of foundation models, multimodal AI, agentic retrieval, and personalized intelligence, shaping the next generation of proactive and intelligent user experiences.
Minimum Qualifications
Master degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
5+ years of industry or research experience developing machine learning systems.
Background in machine learning, deep learning, natural language processing, information retrieval, search, recommender systems, or generative AI.
Experience training, fine-tuning, or deploying transformer-based models and large language models.
Experience with modern deep learning architectures and techniques, including transformers, embeddings, representation learning, and neural ranking.
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.
Ability to work onsite in Cupertino, California, in accordance with Apple's applicable work policies.
Preferred Qualifications
Master's or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
Experience optimizing machine learning models for resource-constrained environments, including knowledge distillation, model compression, quantization, and pruning.
Experience with on-device machine learning or edge AI, or mobile inference frameworks, including optimizing models for latency, memory, compute, and power constraints.
Experience distilling capabilities from large foundation models into small language models or task-specific models for efficient inference.
Experience building retrieval-augmented generation, vector search, embedding retrieval, neural reranking, or semantic search systems.
Experience with query understanding, query rewriting, intent classification, personalized retrieval, learning-to-rank, or recommendation models.
Experience working with transformer architectures and foundation model families such as BERT, T5, Llama, Gemma, Mistral, or related architectures.
Experience evaluating language models, designing AI quality metrics, and building automated and human-in-the-loop evaluation pipelines.
Experience building large-scale production search, recommendation, personalization, or generative AI systems.
Familiarity with multimodal foundation models, tool use, agentic AI, or agentic retrieval systems.
Strong understanding of the tradeoffs among model quality, latency, memory, power consumption, privacy, and reliability for production on-device AI systems.
Ability to prototype new ideas, conduct rigorous experiments, solve ambiguous technical problems, and translate research advances into production-quality machine learning solutions.
