Applied Machine Learning Engineer (NLP/AI)

- Architect, develop, and uphold AI/ML solutions to facilitate NIH grant application processes, peer review workflows, and data analytics. - Employ NLP and machine learning techniques (such as embeddings, classification, clustering, and similarity analysis) for tasks including reviewer-application alignment, keyword extraction, and document examination. - Construct, assess, and progressively enhance machine learning models using both structured and unstructured data, encompassing text, documentation, and images where relevant. - Design and execute comprehensive ML workflows from data ingestion through to output generation. - Troubleshoot, evaluate, and fine-tune ML pipelines and tools to guarantee reliable, consistent, and reproducible outcomes. - Refactor and enhance code to boost performance, scalability, and maintainability. - Manage extensive and intricate datasets, including establishing data validation, quality checks, and preprocessing strategies. - Conduct experiments to assess model efficacy, analyze results, and refine methodologies utilizing both quantitative and qualitative insights. - Collaborate with diverse teams (data scientists, analysts, program staff, and engineers) to transform business requirements into actionable AI/ML solutions. - Ensure reproducibility and transparency through thorough documentation, version control, and structured workflows. - Clearly communicate methods, results, and limitations to both technical and non-technical stakeholders. - Remain informed about advancements in applied AI/ML, particularly in NLP, embeddings, and generative AI, and assess their relevance for NIH applications.