Manager, Applied AI, Advanced Informatics

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, 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 workflows
- Track advances in foundation models, LLMs, and retrieval-augmented generation, and apply them to biomedical/health data
- Document research findings and contribute to internal reports, publications, or conference presentations
- Explain model behavior, limitations, and performance to technical and non-technical teams

This Role Requires
- Bachelor’s in CS/ML/Data Science/Biomedical Informatics/Statistics or related; Master’s/Ph.D. strongly preferred with 4–6+ years applied AI/ML (Ph.D. research experience considered)
- Grounding in supervised, unsupervised, and self-supervised learning; deep neural networks; modern ML frameworks (PyTorch/TensorFlow or equivalent)
- Strong Python skills (scikit-learn, HuggingFace Transformers, pandas, NumPy)
- Experience designing/evaluating NLP or multimodal models, including LLM fine-tuning or prompt engineering
- Experiment tracking/versioning/reproducibility (MLflow, W&B, DVC, or similar)
- Familiarity with cloud ML (AWS SageMaker/GCP Vertex AI/Azure ML or equivalent)
- Experience with health/life sciences data (EHR/EMR, claims, clinical notes, genomic, imaging)
- Familiarity with medical terminologies/ontologies (SNOMED CT, ICD-10/11, LOINC, RxNorm, OMOP CDM)
- Published/open-source work in applied ML/NLP/computational biomedicine
- MLOps/CI/CD for ML or model observability in production
- Familiarity with federated learning, privacy-preserving ML, or regulated data use agreements