Associate Manager, Production AI/ML Engineering, Advanced Informatics
Regeneron
August 12, 2026
On-site
Tarrytown, NY
IT
Discover Your Role:
- Architect and maintain a scalable AWS/Kubernetes ecosystem supporting model training, batch inference, and real-time serving.
- Design and maintain production-grade ML pipelines over population-scale clinical and claims data (ingestion, normalization, transformation across SNOMED CT, ICD-10/11, LOINC, RxNorm, OMOP CDM).
- Develop RESTful APIs to expose model inference to downstream teams and applications.
- Operationalize Applied AI models (deployment, versioning, rollback, A/B testing).
- Build MLOps infrastructure (CI/CD, feature stores, model registries) and champion engineering best practices.
- Own monitoring, alerting, and incident response for production ML systems.
- Partner with data governance/compliance to meet data use agreements and de-identification standards.
- Shape capacity planning and the long-term technical roadmap for the AI/ML platform.
This Role Requires:
- BS in Computer Science/Software Engineering/Data Science/Biomedical Informatics or related (MS preferred) plus 7–9 years progressive ML/platform/software engineering (infrastructure focus).
- Enterprise-scale ML infrastructure experience on AWS (SageMaker/ECS/EC2/S3/Lambda or equivalent).
- Strong Kubernetes expertise (cluster management, scheduling, autoscaling, containerized ML services).
- REST API experience (design, versioning, performance tuning, integration with ML serving).
- Hands-on Postgres (schema design, query optimization, pipeline integration).
- Expert Python and SQL; Git, modular design, testing, CI/CD.
- MLOps tools: MLflow/W&B, model registries, Docker, Airflow/Prefect (or similar).
- Health/life sciences informatics knowledge (EHR/EMR, claims, clinical notes, or administrative data preferred).
Application:
- Apply now.
Compensation (Salary Range, annually): $109,900.00 - $179,300.00.
- Architect and maintain a scalable AWS/Kubernetes ecosystem supporting model training, batch inference, and real-time serving.
- Design and maintain production-grade ML pipelines over population-scale clinical and claims data (ingestion, normalization, transformation across SNOMED CT, ICD-10/11, LOINC, RxNorm, OMOP CDM).
- Develop RESTful APIs to expose model inference to downstream teams and applications.
- Operationalize Applied AI models (deployment, versioning, rollback, A/B testing).
- Build MLOps infrastructure (CI/CD, feature stores, model registries) and champion engineering best practices.
- Own monitoring, alerting, and incident response for production ML systems.
- Partner with data governance/compliance to meet data use agreements and de-identification standards.
- Shape capacity planning and the long-term technical roadmap for the AI/ML platform.
This Role Requires:
- BS in Computer Science/Software Engineering/Data Science/Biomedical Informatics or related (MS preferred) plus 7–9 years progressive ML/platform/software engineering (infrastructure focus).
- Enterprise-scale ML infrastructure experience on AWS (SageMaker/ECS/EC2/S3/Lambda or equivalent).
- Strong Kubernetes expertise (cluster management, scheduling, autoscaling, containerized ML services).
- REST API experience (design, versioning, performance tuning, integration with ML serving).
- Hands-on Postgres (schema design, query optimization, pipeline integration).
- Expert Python and SQL; Git, modular design, testing, CI/CD.
- MLOps tools: MLflow/W&B, model registries, Docker, Airflow/Prefect (or similar).
- Health/life sciences informatics knowledge (EHR/EMR, claims, clinical notes, or administrative data preferred).
Application:
- Apply now.
Compensation (Salary Range, annually): $109,900.00 - $179,300.00.