Manager, Applied AI, Advanced Informatics

What you'll do

• Frame clinical and business informatics challenges into well-scoped AI/ML problem statements with clear success criteria.
• Design and build ML pipelines including data ingestion, feature engineering, training, and evaluation.
• Run experiments, benchmarks, and ablation studies to validate model performance and guide modeling decisions.
• Integrate models into real informatics workflows by partnering with clinical informatics and data engineering teams.
• Apply foundation models, LLMs, and retrieval-augmented generation to biomedical and health data.
• Document research findings for internal reports, publications, or conference presentations.
• Explain model behavior, limitations, and performance to both technical and non-technical team members.

What we're looking for

• Bachelor's degree in Computer Science, Machine Learning, Data Science, Biomedical Informatics, Statistics, or a related field.
• Master's or Ph.D. strongly preferred with 4–6 years of progressive experience in applied AI/ML (preferred).
• Demonstrated ability to independently develop and evaluate models, including relevant research experience for Ph.D. graduates.
• Solid grounding in supervised, unsupervised, and self-supervised learning, deep neural networks, and modern ML frameworks like PyTorch or TensorFlow.
• Strong Python skills across the scientific ML stack including scikit-learn, HuggingFace Transformers, pandas, and NumPy.
• Experience designing and evaluating NLP or multimodal models, including LLM fine-tuning or prompt engineering.
• Experience with health or life sciences data such as EHR/EMR, claims, clinical notes, genomic, or imaging data.
• Familiarity with medical terminologies and ontologies such as SNOMED CT, ICD-10/11, LOINC, RxNorm, or OMOP CDM.