ML Infrastructure Engineer

Responsibilities

• Design and build a centralized system for versioning training data, generated datasets, and model artifacts
• Develop and maintain reliable, reproducible ML training and data generation pipelines
• Refactor and harden existing training and data generation scripts into composable, testable, and maintainable components
• Create CI/CD workflows for validating data pipelines and model training runs
• Build tooling that enables ML engineers to launch, monitor, and debug training jobs
• Optimize and scale real-time model inference services to meet latency and throughput requirements
• Own the deployment path from trained model artifact to production endpoint

Requirements

• 3+ years of work experience in relevant fields
• Bachelor's or Master's degree in Computer Science, Engineering, or equivalent experience
• Experience designing and building data versioning, artifact management, or dataset lineage systems
• Hands-on experience with ML pipeline orchestration tools
• Experience with model serving and inference optimization
• Ability to read and refactor ML training code
• Proficient with Python and PyTorch

Preferred Qualifications

• Familiarity with AWS infrastructure services
• Experience with containerized ML workflows and GPU-accelerated training environments
• Experience with model optimization techniques (e.g., quantization, TensorRT, ONNX Runtime)
• Knowledge of infrastructure-as-code tools (e.g., AWS CDK, Terraform)
• Experience building or operating ML systems that handle large unstructured datasets