Sr Machine Learning Engineer, Proactive

At Apple machine learning powers experiences that anticipate what people need before they ask. Were 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 this role youll 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 Apples ecosystem.

Youll 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. Youll 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. Youll 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. Youll 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 search relevance and user behavior to design evaluation methodologies offline benchmarks and online metrics that measure retrieval quality ranking personalization and language model scalable experimentation and evaluation pipelines for LLMs and search models including model quality robustness latency efficiency and end-to-end product train fine-tune distill and optimize transformer-based language models and foundation models for efficient on-device LLM fine-tuning and post-training approaches including supervised fine-tuning instruction tuning preference optimization parameter-efficient fine-tuning and task-specific and prototype approaches for on-device generative AI including knowledge distillation model compression quantization pruning and low-latency techniques to transfer capabilities from large foundation models into compact on-device models while balancing model quality latency memory footprint power consumption and compute 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.n5 years of industry or research experience developing machine learning in machine learning deep learning natural language processing information retrieval search recommender systems or generative training fine-tuning or deploying transformer-based models and large language with modern deep learning architectures and techniques including transformers embeddings representation learning and neural skills in Python and/or C/C with experience building production-quality software using modern machine learning frameworks such as PyTorch JAX or to work onsite in Cupertino California in accordance with Apples applicable work policies.

Masters or Ph.D. in Computer Science Machine Learning Artificial Intelligence or a related optimizing machine learning models for resource-constrained environments including knowledge distillation model compression quantization and with on-device machine learning or edge AI or mobile inference frameworks including optimizing models for latency memory compute and power distilling capabilities from large foundation models into small language models or task-specific models for efficient building retrieval-augmented generation vector search embedding retrieval neural reranking or semantic search with query understanding query rewriting intent classification personalized retrieval learning-to-rank or recommendation working with transformer architectures and foundation model families such as BERT T5 Llama Gemma Mistral or related evaluating language models designing AI quality metrics and building automated and human-in-the-loop evaluation building large-scale production search recommendation personalization or generative AI with multimodal foundation models tool use agentic AI or agentic retrieval understanding of the tradeoffs among model quality latency memory power consumption privacy and reliability for production on-device AI to prototype new ideas conduct rigorous experiments solve ambiguous technical problems and translate research advances into production-quality machine learning solutions.