Manager, Applied AI, Advanced Informatics

Manager, Applied AI

At Regeneron, we use science and innovation to develop life-changing medicines for people with serious diseases. We are seeking a Manager, Applied AI to join our Advanced Informatics team, supporting health data systems and analytical pipelines across the organization. In this role, you will design and validate applied AI/ML solutions against complex health data and clinical ground truth, while collaborating with senior applied AI leaders, clinical informaticists, data engineers, and production ML engineers to build reusable analytical capabilities the broader informatics team depends on. This position offers the opportunity to contribute to a fast-growing, science-driven organization making a meaningful difference to patients worldwide.

Location: Tarrytown, NY, Armonk, NY, or Warren, NJ

Discover Your Role:

• Frame clinical and business informatics challenges into well-scoped AI/ML problem statements with clear success criteria

• Design and build ML pipelines — data ingestion, feature engineering, training, and evaluation — alongside production engineering

• Run experiments, benchmarks, and ablation studies to validate model performance and guide modeling decisions

• Partner across clinical informatics, data engineering, and ML engineering to bring models into real informatics workflows

• Track advances in foundation models, LLMs, and retrieval-augmented generation, and apply them to biomedical and health data

• Document research findings and contribute to internal reports, publications, or conference presentations

• Explain model behavior, limitations, and performance in terms both technical and non-technical team members can act on

This Role Requires:

• Bachelor's degree in Computer Science, Machine Learning, Data Science, Biomedical Informatics, Statistics, or related field; Master's or Ph.D. strongly preferred with 4–6 years + of progressive experience in applied AI/ML, with demonstrated ability to independently develop and evaluate models (Ph.D. graduates with relevant research experience may be considered)

• Solid grounding in supervised, unsupervised, and self-supervised learning, deep neural networks, and modern ML frameworks (PyTorch, TensorFlow, or equivalent)

• Strong Python skills across the scientific ML stack (scikit-learn, HuggingFace Transformers, pandas, NumPy)

• Experience designing and evaluating NLP or multimodal models, including LLM fine-tuning or prompt engineering

• Comfort with experiment tracking, model versioning, and reproducible research practices (MLflow, W&B, DVC, or similar)

• Familiarity with cloud-based ML infrastructure (AWS SageMaker, GCP Vertex AI, Azure ML, or equivalent)

• Experience with health or life sciences data — EHR/EMR, claims, clinical notes, genomic, or imaging data

• Familiarity with medical terminologies and ontologies (SNOMED CT, ICD-10/11, LOINC, RxNorm, OMOP CDM)

• Published work or open-source contributions in applied ML, NLP, or computational biomedicine

• Experience with MLOps, CI/CD for ML, or model observability in production

• Familiarity with federated learning, privacy-preserving ML, or regulated-environment data use agreements