Senior 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.

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.