Senior ML Engineer: On-Device AI & Retrieval

Apple Inc.

Santa Clara (CA)

On-site

USD 185,000 - 325,000

Full time

3 days ago

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Benefits offered by this job

Apple Benefits

Employee Stock Purchase Plan

Relocation assistance

Tuition reimbursement

Job summary

Apple Inc. in Santa Clara, California, seeks a Senior Machine Learning Engineer to design, train, fine-tune, and deploy transformer-based language models and retrieval systems for efficient on-device deployment with privacy-preserving, personalized experiences across Apple’s ecosystem.

You will collaborate with engineers, researchers, product managers, and designers to advance foundation models, multimodal AI, and agentic retrieval, delivering scalable AI-powered search and proactive

Qualifications

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.

Responsibilities

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.