ML Engineer - Predictive Analytics

About This Role

Tile Health’s predictive analytics engine runs dozens of models that surface early warnings for clinical deterioration, no-show probability, and resource demand forecasting. The ML Engineer will bridge the gap between data science research and production systems by building robust ML infrastructure, model serving pipelines, and monitoring frameworks. You will ensure our models are performant, reliable, and continuously improving in real-world clinical environments.

What You'll Do

• Design and implement ML training and serving pipelines using tools such as MLflow, Kubeflow, or SageMaker
• Optimize model inference latency and throughput for real-time clinical scoring endpoints
• Build automated model retraining and validation workflows that detect concept drift and trigger human review
• Implement feature stores that serve consistent feature sets across training and inference environments
• Collaborate with data scientists to translate research notebooks into production-grade, tested, and versioned model code

What We're Looking For

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field
• 4+ years of software engineering experience with at least 2 years focused on ML infrastructure
• Strong proficiency in Python with production experience in PyTorch or TensorFlow
• Hands-on experience with containerized ML workloads on Kubernetes or managed ML platforms
• Solid understanding of ML lifecycle management including experiment tracking, model versioning, and A/B deployment
• Familiarity with data privacy considerations when training models on protected health information

Nice to Have

• Experience with healthcare-specific ML applications such as readmission prediction or clinical NLP
• Contributions to open-source ML tooling or frameworks
• Knowledge of model interpretability techniques (SHAP, LIME, attention visualization)
• Experience with GPU cluster management and distributed training