Machine Learning Engineer, Apple Services Engineering

Apple Apple · Big Tech · Seattle, WA +2 · Machine Learning and AI

Machine Learning Engineer at Apple Services Engineering focused on designing, building, and deploying machine learning pipelines for personalization systems across Apple's media offerings. The role involves scaling recommender systems for billions of users, optimizing model inference, and implementing evaluation frameworks. Requires strong software engineering fundamentals and experience with large-scale ML models and big data technologies.

What you'd actually do

  1. Design, build, and maintain scalable machine learning pipelines and infrastructure for training and serving personalization models.
  2. Partner closely with ML researchers to transition prototype models into highly optimized, production-ready systems.
  3. Optimize model inference for low latency and high throughput to meet the rigorous demands of Apple’s global user base.
  4. Implement robust monitoring, A/B testing infrastructure, and evaluation frameworks to ensure model quality and reliability in production.
  5. Ship production-quality code and drive engineering best practices, system architecture, and code quality within the team.

Skills

Required

  • Machine learning pipelines
  • Personalization systems
  • Recommender systems
  • Large-scale ML models
  • Model inference optimization
  • Low latency serving
  • High throughput
  • Monitoring
  • A/B testing
  • Evaluation frameworks
  • Production-quality code
  • System architecture
  • Code quality
  • Software engineering fundamentals
  • Python
  • PyTorch
  • scikit-learn
  • numpy-scipy-pandas
  • Deep learning
  • Generative AI
  • Big data technologies
  • Data processing pipelines
  • Distributed computing
  • Spark
  • Hadoop
  • Kafka
  • ML infrastructure

Nice to have

  • MLOps
  • Search ranking infrastructure

What the JD emphasized

  • building, scaling, and deploying recommendation systems or large-scale ML models
  • model optimization, and serving models at scale with low latency

Other signals

  • personalization systems
  • recommender systems
  • large-scale ML models
  • ML pipelines
  • model inference optimization