Senior Machine Learning Engineer - Ai, Search & Knowledge (ml Hub Core)

Apple Apple · Big Tech · Cupertino, CA +1 · Machine Learning and AI

Senior ML Engineer at Apple focused on building infrastructure and tools for the GenAI lifecycle, from experimentation to production inference, emphasizing observability, reproducibility, and modularity. The role involves contributing to architectural direction and designing for flexibility across future projects, with a strong focus on scalable data/compute infrastructure.

What you'd actually do

  1. Design and build infrastructure to support AI features that empowers billions of Apple customers
  2. Develop tools used internally at Apple to build AI experiences through the entire lifecycle - from experimentation to production inference emphasizing observability, reproducibility, and modularity
  3. Work cross functionally with MLE, DS, Infra and others, staying on the cutting edge of tooling that supercharges ML/AI innovation at Apple
  4. Contribute to architectural direction for Apple’s broader AI ecosystem designing for flexibility across future projects

Skills

Required

  • 8+ years of experience in ML engineering, software engineering or applied AI roles
  • Solid understanding of machine learning fundamentals, especially around large models, embeddings, and retrieval systems
  • Proven experience building production-grade ML systems or intelligent data-driven products
  • Proficiency in Python and advanced knowledge of the Python ML ecosystem
  • Extensive experience building tools for ML lifecycle and launching ML-powered experiences to production
  • Strong background in distributed systems, APIs, and scalable data/compute infrastructure
  • Solid understanding of software engineering principles and design patterns
  • Excellent communication skills and ability to work in a collaborative environment
  • Experience with building on top of AWS or GCP cloud services

Nice to have

  • Experience working with notebook ecosystems and web-based IDEs
  • Experience with building and deploy ML software in production to a wide range of infrastructure (CPU/GPU/TPU)
  • Big data experience such as Apache Spark, Apache Ray
  • Familiarity with MLOps tools (e.g., MLflow, Weights & Biases, Ray, Airflow)
  • Some frontend development experience with Javascript / React
  • Prior experience building an Notebook solution in a large organization
  • Experience with building customer facing SDKs in Python

What the JD emphasized

  • production inference
  • observability
  • reproducibility
  • modularity
  • ML lifecycle tools
  • scalable data/compute infrastructure

Other signals

  • GenAI lifecycle
  • production inference
  • observability
  • reproducibility
  • modularity
  • ML lifecycle tools
  • scalable data/compute infrastructure