Senior ML Engineer — On-Device AI & Personalization

Apple Inc.

Cupertino, Northern (CA, KY)

Hybrid

USD 185,000 - 325,000

Full time

41 hours ago
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Benefits offered by this job

Medical and dental coverage
Employee stock purchase plan
Restricted stock unit awards
Relocation assistance

Job summary

Apple Inc. is seeking a Senior Machine Learning Engineer to advance proactive, on-device AI capabilities across the Apple ecosystem.

You will design, train, and deploy transformer-based language models, embedding retrieval, and RAG systems with a focus on privacy and low latency. You will collaborate with cross-functional teams to push research into production, optimize models for resource constraints, and explore distillation, quantization, and adaptable foundation models for on-device

Qualifications

  • Master's or PhD in a related field.
  • 5+ years of industry or research experience.
  • Experience training, fine-tuning and deploying transformer models.
  • Strong background in NLP, IR, and generative AI.
  • Proficiency in Python and C/C++ and ML frameworks.

Responsibilities

  • Design, train, fine-tune, and optimize transformer-based language models for on-device deployment.
  • Develop semantic retrieval, embedding, reranking, and RAG systems.
  • Build scalable pipelines for LLMs, search models, and evaluation.
  • Collaborate with engineers, researchers, product managers and designers.
  • Exploration of foundation models, distillation, quantization, and low-latency inference.

Education

Master's degree in Computer Science / ML / AI or related field
PhD in Computer Science / Machine Learning / AI (preferred)
5+ years experience building ML systems
Experience with transformer models / LLMs
Knowledge of on-device AI / edge AI
Python and C/C++
PyTorch, TensorFlow, JAX

Tools

PyTorch
TensorFlow
JAX

Job description

Apple Inc. is seeking a Senior Machine Learning Engineer to advance proactive, on-device AI capabilities across the Apple ecosystem.

You will design, train, and deploy transformer-based language models, embedding retrieval, and RAG systems with a focus on privacy and low latency. You will collaborate with cross-functional teams to push research into production, optimize models for resource constraints, and explore distillation, quantization, and adaptable foundation models for on-device

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