ML Infrastructure Engineer

Responsibilities

• Design and build systems for versioning training data, generated datasets, and model artifacts with end-to-end lineage tracking.
• Develop reliable and reproducible ML training and data-generation pipelines.
• Refactor and harden ML training and data-generation scripts into composable, testable, and maintainable components.
• Create CI/CD workflows with automated correctness checks and regression detection for data pipelines and model training runs.
• Build tooling for ML engineers to launch, monitor, and debug training jobs.
• Optimize and scale real-time model inference services through latency profiling, batching, and resource-efficient serving.
• Own reliable deployment, rollout, rollback, and monitoring processes from trained model artifacts to production endpoints.

Requirements

• At least 3 years of relevant work experience.
• Bachelor’s or master’s degree in Computer Science, Engineering, or equivalent experience.
• Strong communication skills and ability to collaborate with ML researchers and engineers.
• Experience building data versioning, artifact management, or dataset lineage systems such as DVC, LakeFS, Weights & Biases, or custom solutions.
• Hands-on experience with ML pipeline orchestration tools such as Airflow, Prefect, or Metaflow.
• Experience with model serving and inference optimization, including latency profiling, memory reduction, or serving infrastructure scaling.
• Ability to read and refactor ML training code without necessarily designing model architectures.
• Proficiency with Python and PyTorch.
• Preferred experience with AWS infrastructure, containerized ML workflows, GPU-accelerated training, model optimization, infrastructure-as-code, and large unstructured datasets.