Appleposted 18 days ago
Senior
Cupertino, CA

About the position

In this highly visible role, your primary responsibilities will include developing LLM components for use in generative AI applications. You will collaborate with our internal multi-functional design teams as well as the AIML organization at Apple to understand domain-specific needs and tailor AI solutions to these domains. Additionally, you will serve as the point of contact for customers, resolving technical issues, and providing insights on LLM infrastructure improvements. Your role will also involve enabling the organization to leverage data and drive efficiency in chip delivery.

Responsibilities

  • Developing LLM components for use in generative AI applications.
  • Collaborate with internal multi-functional design teams and the AIML organization to understand domain-specific needs.
  • Tailor AI solutions to specific domains.
  • Serve as the point of contact for customers, resolving technical issues.
  • Provide insights on LLM infrastructure improvements.
  • Enable the organization to leverage data and drive efficiency in chip delivery.

Requirements

  • Python programming experience.
  • Hands-on experience in NLP and Data Science principles (e.g., indexing knowledge, pre-processing data, or fine-tuning models).
  • Knowledge of current Gen AI research in areas such as RAG, Semantic Search, Agents, or Prompt Engineering.
  • Minimum requirement of BS and 10+ years of relevant industry experience.

Nice-to-haves

  • Experience in designing and implementing information retrieval systems using embeddings (e.g., MiniLM), vector stores (e.g., Milvus, Qdrant), or similarity match & ranking techniques.
  • Strong background in ML related software engineering such as infrastructure, frameworks or tools.
  • Proficiency in articulating technical and architectural challenges in a precise manner.
  • Experience collaborating with partners to develop and iterate on solutions.
  • Designed and optimized RESTful services.
  • Comfort within Linux/Unix environments.
  • Understanding of software engineering practices (agile, code review, automated builds, regressions testing).
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