Staff Machine Learning Engineer, Search & Knowledge Platform

Apple Apple · Big Tech · Seattle, WA · Software and Services

Staff Machine Learning Engineer at Apple focused on Search and Knowledge Platform, developing next-generation Search and Question Answering systems using LLMs and RAG. Responsibilities include query understanding, retrieval, ranking, fine-tuning, and deploying models at scale, with a focus on production systems and online inference optimization.

What you'd actually do

  1. Understanding product requirements then translating them into modeling and engineering tasks
  2. Analyzing search ranking and relevance requirements, issues, and opportunities
  3. Utilizing PyTorch, TensorFlow, or JAX for training and deploying deep learning models
  4. Building ML models for retrieval, relevance ranking, and query understanding

Skills

Required

  • MS in Computer Science or related field
  • 10+ years of work experience in machine learning, deep learning or related field
  • 10+ years experience in shipping Search and Q&A technologies and ML systems
  • Excellent programming skills in mainstream programming languages such as C++, Python, Scala, and Go
  • Experience delivering tooling and frameworks to evaluate individual components and end-to-end quality
  • Strong analytical skills to systematically identify opportunities to improve search relevance and answer accuracy
  • Strong written and verbal communication with the ability to articulate complex topics
  • Excellent interpersonal skills and teamwork; demonstrated ability to connect and collaborate with others

Nice to have

  • PhD in Computer Science, Artificial Intelligence, Machine Learning, Information Retrieval, Data Science or related field
  • Strong industry background and experience in search and related technologies (LLMs, Machine Learning, NLP, Information Retrieval, Question Answering)
  • Strong and validated experience of ML development and production systems
  • Experience working with foundation models and LLMs

What the JD emphasized

  • shipping Search and Q&A technologies and ML systems
  • production systems
  • large scale machine learning
  • deep learning models
  • online learning
  • natural language processing
  • petabytes of data
  • LLM and RAG
  • training, fine-tuning and deploying these models at scale
  • online inference optimization

Other signals

  • shipping production ML models
  • large scale machine learning
  • deep learning techniques
  • LLM and RAG
  • training, fine-tuning and deploying models at scale
  • online inference optimization