Sr 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 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.

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.

Build semantic retrieval, embedding, reranking, and retrieval-augmented generation systems, along with models for query understanding, intent prediction, personalization, retrieval, and ranking. Analyze search relevance and user behavior to design evaluation methodologies, offline benchmarks, and online metrics that measure retrieval quality, ranking, personalization, and language model performance. Build scalable experimentation and evaluation pipelines for LLMs and search models, including model quality, robustness, latency, efficiency, and end-to-end product metrics.

Design, train, fine-tune, distill, and optimize transformer-based language models and foundation models for efficient on-device deployment. Develop LLM fine-tuning and post-training approaches, including supervised fine-tuning, instruction tuning, preference optimization, parameter-efficient fine-tuning, and task-specific adaptation. Research and prototype approaches for on-device generative AI, including knowledge distillation, model compression, quantization, pruning, and low-latency inference. Develop techniques to transfer capabilities from large foundation models into compact on-device models while balancing model quality, latency, memory footprint, power consumption, and compute constraints.

Partner with engineers, researchers, product managers, and designers to bring AI capabilities from research into production, driving technical strategy across projects and exploring new applications of foundation models, multimodal AI, agentic retrieval, and personalized intelligence.

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 Tensor Flow. Ability to work onsite in Cupertino, California, in accordance with Apple's applicable work policies.

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…