ML Infrastructure Engineer

Job Description

Scale ML infrastructure to serve millions of users globally. Build the platform that powers Netflix's recommendation systems and content optimization algorithms.

Requirements

• BS/MS in Computer Science, Engineering, or related field
• 4+ years of experience in ML infrastructure and platform engineering
• Proficiency in Kubernetes, Docker, and cloud-native technologies
• Experience with distributed computing frameworks (Spark, Flink)
• Strong background in MLOps, model serving, and monitoring
• Knowledge of streaming data processing and real-time ML systems
• Experience with large-scale distributed systems and microservices

Responsibilities

• Build and maintain ML infrastructure serving millions of users
• Develop MLOps platforms for model training, deployment, and monitoring
• Optimize ML serving infrastructure for low-latency, high-throughput workloads
• Design and implement real-time feature engineering pipelines
• Collaborate with ML engineers on platform requirements and tools
• Ensure reliability, scalability, and cost-efficiency of ML systems
• Contribute to Netflix's internal ML platform and developer experience