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Senior Machine Learning Engineer - Large Language Models (LLMs), Siri Planner Team

Apple

$181,100 - $318,400
Jul 5, 2025
Cupertino, CA, US
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Advance Siri’s natural language understanding and planning capabilities using innovative LLM technologies to revolutionize how millions of Siri users worldwide interact with their Apple devices.

Requirements

  • Proven hands-on experience in machine learning engineering for large-scale models, with a strong focus on generative AI, LLMs, Retrieval Augmented Generation (RAG), or agentic systems.
  • Strong Python proficiency, including development, debugging, and design, coupled with extensive experience using ML frameworks (e.g. PyTorch, Jax, HuggingFace).
  • Applying LLMs for synthetic data generation (e.g. for knowledge distillation) or applying reinforcement learning for post-training or fine-tuning of LLMs.
  • A successful track record of building and deploying end-to-end ML data pipelines (data preparation, storage, training, and inference) in cloud or on-premise environments.
  • Experience with training, fine-tuning, and deploying LLMs in production environments.
  • Proficiency in evaluating LLMs for specific product tasks and performance metrics.

Responsibilities

  • Developing innovative systems for synthetic training data generation and implementing strategies for the continuous optimization of model performance.
  • Designing and implementing agentic workflows and RAG systems to enhance Siri’s capabilities.
  • Optimizing model performance for tool calling and reasoning tasks.
  • Actively staying at the forefront of academic and industry research in LLMs, NLP, and agentic systems, and translating novel insights into practical solutions.
  • Collaborating closely with a multidisciplinary team of researchers, software engineers, and product designers to seamlessly integrate AI innovations into the Siri user experience.
  • Applying LLMs for synthetic data generation (e.g. for knowledge distillation) or applying reinforcement learning for post-training or fine-tuning of LLMs.
  • Building and deploying end-to-end ML data pipelines (data preparation, storage, training, and inference) in cloud or on-premise environments.

Other

  • Advanced degree (MSc/PhD) in Machine Learning, Computer Science, or a related quantitative field; or BSc with 5+ years of relevant industry experience.
  • Excellent problem-solving, critical thinking, and interpersonal skills, with a collaborative attitude.