Associate Manager, Production AI/ML Engineering, Advanced Informatics
Regeneron
September 02, 2026
On-site
Tarrytown, NY
IT
When & Where
- Location: Tarrytown or Armonk, NY or Warren, NJ
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, and 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
- Bachelor’s degree in Computer Science/Software Engineering/Data Science/Biomedical Informatics or related (Master’s preferred) with 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, production containerized ML)
- RESTful API experience (design, versioning, performance tuning, ML serving integration)
- Hands-on Postgres (schema design, query optimization, pipeline integration)
- Expert Python and SQL; Git, modular design, testing, CI/CD
- MLOps tooling: MLflow/W&B, model registries, Docker, orchestration (Airflow/Prefect or similar)
- Preferred: health/life sciences informatics knowledge (EHR/EMR, claims, clinical notes, administrative data)
Application Instructions
- Apply now
- Location: Tarrytown or Armonk, NY or Warren, NJ
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, and 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
- Bachelor’s degree in Computer Science/Software Engineering/Data Science/Biomedical Informatics or related (Master’s preferred) with 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, production containerized ML)
- RESTful API experience (design, versioning, performance tuning, ML serving integration)
- Hands-on Postgres (schema design, query optimization, pipeline integration)
- Expert Python and SQL; Git, modular design, testing, CI/CD
- MLOps tooling: MLflow/W&B, model registries, Docker, orchestration (Airflow/Prefect or similar)
- Preferred: health/life sciences informatics knowledge (EHR/EMR, claims, clinical notes, administrative data)
Application Instructions
- Apply now