Job Description

As a Software Engineer on the Apple Data Platform team, you will design and develop orchestration systems for AI, ML, and data workloads across Apple, working with cross-functional teams to deliver scalable infrastructure that accelerates model development and deployment.

Requirements

5+ years of experience in MLOps, DevOps, or related infrastructure roles
Experience working in cross-functional teams and communicating technical concepts to diverse audiences
Experience designing, building, and maintaining ML infrastructure and deployment pipelines using containerization technologies (Docker, Kubernetes preferred) and cloud platforms (AWS, Azure, or GCP)
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)
Proficiency in monitoring and observability tools (Prometheus, Grafana, ELK stack) for ML model performance and system health
Experience with data pipeline orchestration tools (Airflow, Prefect, Dagster) and streaming platforms (Kafka, Kinesis)
Knowledge of ML model versioning, experiment tracking, and feature stores (MLflow, Weights & Biases, Feast)
Experience with automated testing frameworks for ML systems, including data validation and model testing
Understanding of security best practices for ML systems and data governance
Excellent grasp of software engineering fundamentals and DevOps practices
BS, MS in Computer Science, Software Engineering, Machine Learning, or equivalent degree with applicable experience

Responsibilities

Design and develop orchestration systems for AI, ML, and data workloads
Enable real-time, offline, and batch workflows across Apple
Work with cross-functional partners and internal product teams
Deliver reliable, scalable, and easy-to-use infrastructure
Accelerate model development and deployment