Devops/backend Software Engineer

Apple Apple · Big Tech · San Diego, CA · Hardware

DevOps/Backend Engineer role focused on building and maintaining the infrastructure for AI/ML and Generative AI services at Apple's scale. This includes cloud infrastructure, CI/CD pipelines, and evaluation workflows to support hardware product development teams. The role involves collaborating with ML engineering teams to integrate and operationalize models, ensuring reliable pipelines for evaluation, A/B testing, and performance benchmarking.

What you'd actually do

  1. Design, build, maintain, and manage cloud infrastructure platforms using IaC to service cutting edge machine learning workflows
  2. Collaborate with ML engineering partners to integrate and operationalize hosted models into our services, building reliable pipelines for model evaluation, A/B testing, and performance benchmarking
  3. Develop and maintain infrastructure for GenAI-powered evaluation and testing workflows, ensuring our internal tooling can assess and compare models across different parts of our systems
  4. Write high-quality code that’s testable, scalable, and able to be maintained by others
  5. Work closely with software developers in our team providing infrastructure expertise, cloud integration best-practices, and service architecture guidance

Skills

Required

  • SRE/DevOps
  • systems engineering
  • build/release/deployment
  • automation
  • Kubernetes
  • Docker
  • database platforms
  • event/data pipelines
  • model-serving or API integration patterns
  • CI/CD pipelines
  • networking load balancers
  • Python
  • Ruby
  • Java

Nice to have

  • scalable, maintainable, robust web-services and applications
  • architect complex systems
  • Generative AI services infrastructure
  • model integration
  • evaluation pipelines
  • A/B testing frameworks
  • MLOps practices
  • model versioning
  • pipeline orchestration
  • experiment tracking
  • performance monitoring

What the JD emphasized

  • AI/ML and Generative AI services
  • infrastructure
  • evaluation pipelines
  • operationalize models

Other signals

  • AI/ML services infrastructure
  • Generative AI services infrastructure
  • MLOps
  • operationalize models
  • evaluation pipelines