Job Description

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

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