How to become a Machine Learning Infrastructure Engineer
Builds and operates the infrastructure, platforms, pipelines, and tooling that move machine learning models from experimentation into reliable, scalable production systems. The role spans data and training pipelines, model deployment and serving, observability, lifecycle management, cloud infrastructure, automation, and production reliability.
Top Skills Required
- Machine learning operations
- ML infrastructure architecture
- Data pipelines
Education & Certifications
Typical: Bachelor's or master's degree in computer science, machine learning, software engineering, systems, or a related technical field.
Alternative paths:
- Equivalent practical industry experience
- Significant experience in infrastructure, platform engineering, DevOps, MLOps, backend engineering, or data engineering
- Research or project experience in machine learning systems, distributed systems, or large-scale data processing