Manager, Applied AI, Advanced Informatics

About the role

The Manager, Applied AI is a research-facing technical role within the Advanced Informatics team responsible for designing and developing applied AI solutions across health data systems and analytical pipelines. This role works closely with senior applied AI leaders to formulate ML problems against complex health data, validate methods against clinical ground truth, and build reusable analytical capabilities that the broader informatics team depends on.

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.
• Minimum Years of Experience: 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.

Qualifications

• 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.

Skills & Abilities

• Knowledge, Skills & Abilities (Required)
• Knowledge, Skills & Abilities (Preferred)