LLM Machine Learning Engineer

Apple-posted 5 months ago
Mid Level
San Diego, CA
5,001-10,000 employees
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Imagine what you could do here. At Apple, we believe new insights have a way of becoming excellent products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. The people here at Apple don’t just create products — they create the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it. It takes deeply dedicated, intelligent individuals to maintain and exceed the high expectations at Apple. The Product Operations team is looking for an extraordinary engineer to join our team. You will help design and implement our machine learning strategy to the substantial supply chain and help build the future of our manufacturing systems and smarter factories. We will be collaborating and working with multi-functional teams and applying algorithms to large-scale data.

  • Lead development of machine learning solutions for product operations.
  • Deliver projects from end-to-end: problem statement and conceptualization, proof-of-concept, and participation in final deployment.
  • Perform ad-hoc statistical analyses.
  • Work closely with data engineers to generate detailed business intelligence solutions.
  • Conduct presentations of analyses to a wide range of audiences including executives.
  • 3+ years of experience in machine learning algorithms, statistics, and data mining models, with an emphasis on large language models (LLM) or large multimodal models (LMM).
  • Master’s degree in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Operations Research, Physics, Mechanical Engineering, Electrical Engineering, or a related field.
  • Proven experience in LLM and LMM development, fine-tuning, and application building.
  • Experience with agents and agentic workflows.
  • Experience with modern LLM serving and inference frameworks, including vLLM for efficient model inference and serving.
  • Hands-on experience with LangChain and LlamaIndex, enabling RAG applications and LLM orchestration.
  • Strong software development skills with proficiency in Python.
  • Experienced user of ML and data science libraries such as PyTorch, TensorFlow, Hugging Face Transformers, and scikit-learn.
  • Familiarity with distributed computing, cloud infrastructure, and orchestration tools, such as Kubernetes, Apache Airflow (DAG), Docker, Conductor, Ray for LLM training and inference at scale.
  • Deep understanding of transformer-based architectures (e.g., BERT, GPT, LLaMA) and their optimization for low-latency inference.
  • Ability to meaningfully present results of analyses in a clear and impactful manner, breaking down complex ML/LLM concepts for non-technical audiences.
  • Experience applying ML techniques in manufacturing, testing, or hardware optimization.
  • Proven experience in leading and mentoring teams.
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