Apple’s Data Platform powers the machine learning, AI, and data services that enable intelligent experiences across Apple products, and this role aims to build the unified orchestration layer that powers large-scale data and ML workflows across the company
Requirements
- Experience with React, NodeJS and ES6 concepts
- Experience with a modern front-end build tool, e.g. Webpack
- End-to-End Web application development experience
- Good knowledge of APIs and REST architecture
- Proficient knowledge of Git and collaborative development workflows
- Proficient coding skills in Python, Go, or Scala with experience in ML frameworks (TensorFlow, PyTorch, MLflow, Kubeflow)
- Strong experience with Infrastructure as Code (Terraform, CloudFormation) and CI/CD tools (Jenkins, GitLab CI, GitHub Actions)
Responsibilities
- design and develop orchestration systems that enable real-time, offline, and batch workflows for AI, ML, and data workloads across Apple
- work with cross-functional partners and internal product teams to deliver reliable, scalable, and easy-to-use infrastructure that accelerates model development and deployment
- design and implement scalable systems that enable Apple teams to train models, analyze data, and deploy AI at Apple scale with strong governance
- build the unified orchestration layer that powers large-scale data and ML workflows across the company
- deliver reliable, scalable, and easy-to-use infrastructure that accelerates model development and deployment
- work with cutting-edge open source technologies such as Ray and Spark
- enable Apple teams to train models, analyze data, and deploy AI at Apple scale with strong governance
Other
- 5+ years of experience in MLOps, DevOps, or related infrastructure roles
- Experience working in cross-functional teams and communicating technical concepts to diverse audiences
- BS, MS in Computer Science, Software Engineering, Machine Learning, or equivalent degree with applicable experience
- Excellent grasp of software engineering fundamentals and DevOps practices
- Understanding of security best practices for ML systems and data governance
