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About the Role

The Manager, Applied AI is a research-facing technical role responsible for designing and developing applied AI solutions across health data systems and analytical pipelines. This role collaborates with senior leaders to formulate ML problems, validate methods, and build reusable analytical capabilities.

Responsibilities

• Design and develop applied AI/ML solutions for health informatics use cases, from problem framing and data exploration through model development and evaluation.
• Translate business and clinical informatics challenges into well-scoped AI/ML problem statements with clear success criteria.
• Build and maintain ML pipelines including data ingestion, feature engineering, model training, and evaluation, in close collaboration with production engineering.
• Conduct experiments, benchmarking, and ablation studies to validate model performance and inform modeling decisions.
• Partner with clinical informaticists, data engineers, and production AI/ML engineers to integrate models into informatics workflows.
• Stay current with advances in foundation models, LLMs, retrieval-augmented generation, and their application to biomedical and health data domains.
• Contribute to research documentation, internal technical reports, and where appropriate, external publications or conference presentations.
• Communicate model behavior, limitations, and performance clearly to both technical and non-technical stakeholders.

Requirements

• Bachelor's degree (BS) in Computer Science, Machine Learning, Data Science, Biomedical Informatics, Statistics, or a closely related field required.
• Ph.D. or Master's degree strongly preferred given the research-facing nature of this role.
• 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 at the lower end of this range.
• Solid understanding of supervised, unsupervised, and self-supervised learning; deep neural networks; and modern ML frameworks (PyTorch, TensorFlow, or equivalent).
• Strong command of Python and the scientific ML stack (scikit-learn, HuggingFace Transformers, pandas, NumPy, etc.).
• Experience designing and evaluating NLP or multimodal models, including fine-tuning or prompt engineering with large language models.
• Proficiency 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).
• Ability to drive technical work independently and communicate findings clearly across teams.
• Experience with health or life sciences data — EHR/EMR records, claims data, clinical notes, genomic data, or imaging.
• Informatics domain knowledge (strong plus): familiarity with medical terminologies and ontologies (SNOMED CT, ICD-11, LOINC, RxNorm, OMOP CDM) and machinable health data standards.
• Published work or open-source contributions in applied ML, NLP, or computational biomedicine.
• Experience with MLOps pipelines, CI/CD for ML, and model observability in production environments.
• Familiarity with federated learning, privacy-preserving ML, or working with data use agreements in regulated environments.

Skills

• AI/ML theory
• Applied AI/ML
• Machine learning
• Health informatics
• Supervised learning
• Unsupervised learning
• Self-supervised learning
• Deep neural networks
• PyTorch
• TensorFlow
• Python
• scikit-learn
• HuggingFace Transformers
• pandas
• NumPy
• NLP
• Multimodal models
• Large language models
• Prompt engineering
• Experiment tracking
• Model versioning
• Reproducible research practices
• MLflow
• W&B
• DVC
• AWS SageMaker
• GCP Vertex AI
• Azure ML
• Health data
• Life sciences data
• EHR/EMR records
• Claims data
• Clinical notes
• Genomic data
• Imaging data
• Medical terminologies
• Ontologies
• SNOMED CT
• ICD-10/11
• LOINC
• RxNorm
• OMOP CDM
• MLOps pipelines
• CI/CD for ML
• Model observability
• Federated learning
• Privacy-preserving ML
• Data use agreements

Location

• Remote (US)
• Tarrytown, NY
• Armonk, NY
• Warren, NJ

Work Type

• Remote
• On-site

Experience Level

• 4-6 years
• PhD graduates with relevant research experience may be considered at the lower end of this range

Education Level

• Bachelor's degree (BS) in Computer Science, Machine Learning, Data Science, Biomedical Informatics, Statistics, or a closely related field
• Ph.D. or Master's degree strongly preferred

Salary/Compensations

• $150,500.00 - $245,500.00

Benefits

• Annual bonuses or other incentive plans
• Equity awards
• Pension or retirement benefits
• 401(k) company match
• Health and wellness programs
• Fitness centers
• Insurance benefits (e.g. medical, dental, vision, life and disability)
• Paid time off
• Family support benefits

About the Company

• Regeneron is a leading biotechnology company that invents life-transforming medicines for people with serious diseases.
• Founded and led for over 30 years by physician-scientists, our unique ability to repeatedly and consistently translate science into medicine has led to nine FDA-approved treatments and numerous product candidates in development, almost all of which were homegrown in our laboratories.
• Our medicines and pipeline are designed to help patients with eye diseases, allergic and inflammatory diseases, cancer, cardiovascular and metabolic diseases, neurology, infectious diseases and rare diseases.
• Regeneron is also proud of award winning culture of innovation, being recognized as a Great Place to Work in 2021, Fast Company’s Best Workplace for Innovators in 2020, and Forbes JUST companies in 2020, among others.

Equal Opportunity

• Regeneron is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion or belief (or lack thereof), sex, sexual orientation, gender identity or expression, gender reassignment, marital or civil partnership status, civil status, pregnancy or parental status, age, disability, nationality, citizenship status, ethnic or national origin, membership of the Traveler community, familial status, genetic information, military or veteran status, or any other characteristic protected under applicable law.
• Where required, we will provide reasonable accommodation to applicants with known disabilities or chronic illnesses during the recruitment process, unless such accommodation would impose undue hardship.