Staff Machine Learning Engineer

Apple Apple · Big Tech · Cupertino, CA · Software and Services

Staff Machine Learning Engineer at Apple focused on building and scaling ML infrastructure and generative AI platforms. The role involves developing systems for ML data, embeddings, feature workflows, and enabling GenAI applications. Key responsibilities include designing scalable systems, improving experimentation and evaluation, optimizing inference and AI Ops, and prototyping/optimizing GenAI models for production. The role emphasizes end-to-end ML workflow experience, large-scale distributed systems, and modern generative techniques.

What you'd actually do

  1. Design and build scalable systems for ML data, embeddings, and feature workflows
  2. Develop capabilities that improve experimentation, evaluation, and model performance at scale
  3. Partner with research and product teams to enable rapid GenAI feature development
  4. Drive efficiency, reliability, and automation across inference and AI Ops workflows
  5. Prototype and optimize GenAI models, including open-source models, for scalable production use

Skills

Required

  • Strong foundation in machine learning
  • hands-on experience across the end-to-end ML workflow
  • data preparation
  • pipeline development
  • experimentation
  • evaluation
  • deployment
  • Expertise in building and running large scale distributed systems
  • modern generative techniques (e.g. transformers, diffusion, retrieval-augmented generation)
  • Proven experience building and delivering data and machine learning infrastructure in real-world production environments
  • fine-tuning workflows
  • model optimization
  • preparing models for scalable inference
  • generative AI and its applications
  • configuring, deploying and troubleshooting large scale production environments
  • designing, building, and maintaining scalable, highly available systems that prioritize ease of use
  • Extensive programming experience in Java, Python or Go
  • Strong collaboration and communication (verbal and written) skills
  • Comfortable navigating ambiguity and evolving technical landscapes

Nice to have

  • Proficiency in one or more ML frameworks
  • Experience with containerization and orchestration technologies, such as Docker and Kubernetes

What the JD emphasized

  • end-to-end ML workflow
  • large scale distributed systems
  • real-world production environments
  • scalable production use
  • large-scale ML workloads

Other signals

  • ML infrastructure
  • generative AI
  • ML data and features platform
  • embeddings
  • AI Ops
  • efficient inference
  • feature platform
  • foundation models
  • retrieval-augmented systems