Assoc Manager, Production AI/ML Engineering, Advanced Informatics

Regeneron Pharmaceuticals•Tarrytown, GA

1d•$150,500 - $245,500•Onsite

About The Position

At Regeneron, we use science and innovation to develop life-changing medicines for people with serious diseases. We are seeking an Associate Manager, Production AI/ML Engineering to join our Advanced Informatics team. In this role, you will architect and scale the computational ecosystem that powers our AI/ML applications — transforming population-scale clinical and claims data into reliable, production-grade model inference — while collaborating closely with Applied AI managers, clinical informaticists, and data governance partners. This position offers the opportunity to contribute to a fast-growing, science-driven organization making a meaningful difference to patients worldwide.

Requirements

Bachelor's degree in Computer Science, Software Engineering, Data Science, Biomedical Informatics, or related (Master's preferred) with 7–9 years of progressive ML/platform/software engineering experience with an infrastructure focus
Proven experience designing and operating ML infrastructure at enterprise scale on AWS (SageMaker, ECS, EC2, S3, Lambda, or equivalent).
Strong Kubernetes expertise: cluster management, workload scheduling, autoscaling, and deploying containerized ML services in production.
Proficiency building and maintaining RESTful APIs; experience with API design, versioning, performance tuning, and integration with ML serving frameworks.
Hands-on experience with Postgres: schema design, query optimization, and integration into data and ML pipelines.
Expert-level Python and SQL; strong software engineering fundamentals including Git, modular design, testing, and CI/CD.
Proficiency with MLOps tooling: experiment tracking (MLflow, W&B), model registries, containerization (Docker), and pipeline orchestration (Airflow, Prefect, or similar).

Nice To Haves

Informatics knowledge with health or life sciences data — EHR/EMR records, claims data, clinical notes, or administrative data is preferred.

Responsibilities

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, including ingestion, normalization, and transformation across controlled terminologies (SNOMED CT, ICD-10/11, LOINC, RxNorm, OMOP CDM).
Develop RESTful APIs that reliably expose model inference to downstream teams and applications
Operationalize models from the Applied AI team — owning 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 and compliance to ensure pipelines and deployments adhere to data use agreements and de-identification standards.
Shape capacity planning and the long-term technical roadmap for the team's AI/ML platform