ML Infrastructure Engineer

Responsibilities

• Design and build a centralized system for versioning training data, generated datasets, and model artifacts, with full lineage tracking from raw source data through to trained model outputs.
• 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, including automated correctness checks and regression detection.
• Build tooling that enables ML engineers to launch, monitor, and debug training jobs with minimal friction.
• Optimize and scale real-time model inference services to meet latency and throughput requirements in production, including profiling, batching strategies, and resource-efficient serving.
• Own the deployment path from trained model artifact to production endpoint, ensuring reliable rollouts, rollback, and monitoring.

Requirements

• 3+ years of work experience in relevant fields.
• Bachelor's or Master's degree in Computer Science, Engineering, or equivalent experience.
• Strong communication skills and the ability to work closely with ML researchers and engineers to understand their workflows and translate them into robust systems.
• Experience designing and building data versioning, artifact management, or dataset lineage systems (e.g., DVC, LakeFS, Weights & Biases, or custom solutions).
• Hands-on experience with ML pipeline orchestration tools (e.g., Airflow, Prefect, Metaflow, or similar).
• Experience with model serving and inference optimization — profiling latency, reducing memory footprint, or scaling serving infrastructure to meet real-time constraints.
• Ability to read and refactor ML training code — you don't need to design model architectures, but you need to understand what training pipelines are doing well enough to make them reliable.
• Proficient with Python, PyTorch.

Bonus 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, distillation).
• Knowledge of infrastructure-as-code tools (e.g., AWS CDK, Terraform).
• Experience building or operating ML systems that handle large unstructured datasets (imagery, 3D data, sensor data).