Machine Learning Engineer

Apple Apple · Big Tech · Cupertino, CA +1 · Operations and Supply Chain

Machine Learning Engineer to design and build GenAI-powered features and workflows leveraging LLMs and modern AI techniques for supply chain optimization. Responsibilities include end-to-end GenAI capability development, prompt and tool design, agent orchestration, retrieval strategies, model selection, system evaluation, Text-to-SQL development, and establishing guardrails. Will also focus on inference and serving in production.

What you'd actually do

  1. Partner with business and product teams to identify high-impact opportunities and translate ambiguous requirements into GenAI-powered features and workflows delivered through a shared AI platform and embedded across products
  2. Design, build, and own end-to-end GenAI capabilities that support both a centralized AI platform and product teams, covering all aspects from prompt and tool design to agent orchestration, retrieval strategies, model selection, and system evaluation
  3. Develop reliable, scalable, and cost-aware GenAI features in collaboration with platform, data, and application engineering teams, ensuring strong performance, observability, and maintainability in production environments
  4. Establish evaluation and monitoring strategies for GenAI-driven features, focusing on output quality, correctness, safety, and business relevance through offline benchmarks, automated checks, and human-in-the-loop review
  5. Develop Text-to-SQL and structured reasoning capabilities that enable natural-language interaction with structured data, ensuring accuracy, security, and alignment with business semantics

Skills

Required

  • Bachelors degree
  • PhD/MS in Computer Science, Statistics, Applied Math or a related field
  • 5+ years of industry experience
  • Experience with modern deep learning frameworks, such as PyTorch or TensorFlow
  • Hands-on experience working with transformer-based models, including large language models (e.g., GPT style models or BERT-like architectures)
  • Practical experience leveraging LLMs or GenAI models via APIs to create reliable and user-facing features or workflows
  • Familiarity with common GenAI tools and frameworks, such as LangChain or similar
  • Solid understanding of foundational ML concepts including supervised, unsupervised and reinforcement learning
  • Experience with model deployment pipelines and serving GenAI models in production
  • Experience applying modern ML or GenAI techniques in production workflows, including tasks such as Retrieval-Augmented Generation (RAG), structured reasoning, or prompt-based system design

Nice to have

  • Strong problem-solving skills and the ability to tackle ambiguous, real-world challenges, along with clear communication and collaboration skills
  • Ability to operate independently and lead without authority
  • Experience working in Supply Chain, Operations, or a related field

What the JD emphasized

  • GenAI-powered features and workflows
  • agent orchestration
  • retrieval strategies
  • system evaluation
  • Text-to-SQL
  • structured reasoning
  • agentic AI patterns
  • guardrails

Other signals

  • GenAI-powered features and workflows
  • LLMs and modern AI techniques
  • Agent orchestration
  • Retrieval strategies
  • System evaluation
  • Text-to-SQL
  • Structured reasoning
  • Agentic AI patterns
  • Guardrails