Apple’s Data Platform powers the machine learning, AI, and data services that enable intelligent experiences across Apple products. As a Software Engineer focused on MLOps, you’ll help build the unified orchestration layer that powers large-scale data and ML workflows across the company.
Requirements
- 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
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.
- Leveraging cutting-edge open source technologies such as Ray and Spark, you’ll design and implement scalable systems that enable Apple teams to train models, analyze data, and deploy AI at Apple scale with strong governance.
- 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
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
- 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
