NLP & AI Applied Machine Learning Engineer

Role Description: The Government Health and Safety Solutions Operation is on the lookout for a talented Applied Machine Learning Engineer to join our team. This position requires being onsite in Bethesda, MD (with some remote opportunities).

Responsibilities: Design, develop, and maintain innovative AI/ML solutions that enhance NIH grant application intake, peer review processes, and analytical insights. Utilize NLP and machine learning techniques (including embeddings, classification, clustering, and similarity analysis) for tasks such as reviewer-application matching and document analysis. Construct, evaluate, and continually improve machine learning models leveraging both structured and unstructured data, including text and images. Design and implement efficient end-to-end ML pipelines encompassing data ingestion, preprocessing, feature generation, model execution, evaluation, and output delivery. Debug, test, and optimize ML pipelines to ensure reliable, consistent, and reproducible outcomes. Refine and enhance code for improved performance, scalability, and maintainability. Work with complex datasets, implementing data validation, quality checks, and preprocessing workflows. Conduct experiments to assess model performance, analyze findings, and adjust strategies based on quantitative and qualitative results. Collaborate with multidisciplinary teams to translate business needs into effective AI/ML solutions. Ensure transparency and reproducibility through meticulous documentation and structured workflows. Communicate technical methods, results, and limitations clearly to both technical and non-technical stakeholders. Keep abreast of advancements in applied AI/ML, particularly in NLP, embeddings, and generative AI, and assess their relevance to NIH projects.

Required Qualifications: Master’s degree in data science, Computer Science, Computational Linguistics, or a related field (or equivalent experience). 3-5 years of relevant experience in applied machine learning, data science, or a related field. Strong programming skills in Python (preferred) and/or R. Proven experience delivering comprehensive ML solutions, including model development, evaluation, and pipeline implementation. Hands‑on experience developing and applying machine learning models. Familiarity with NLP techniques such as text classification and semantic similarity. Experience working in cloud or shared computing environments (e.g., Azure, Biowulf). Proficiency in building and maintaining data processing or ML pipelines. Experience cleaning, preprocessing, and engineering features from real‑world datasets. Ability to debug, test, and improve complex code and workflows. Knowledge of at least one modern ML framework (e.g., PyTorch, Tensor Flow, scikit‑learn). Strong analytical capabilities and problem‑solving skills. Excellent communication skills for conveying technical concepts to diverse audiences.

Preferred Qualifications: Experience with transformer models or large language models in text analysis or document processing. Familiarity with reviewer matching, recommendation systems, or document similarity challenges. Knowledge of distributed data processing tools (e.g., Spark, Dask). Experience with experiment tracking and reproducible workflows (e.g., MLflow). Familiarity with NIH data systems or scientific research datasets. Experience with medical or scientific imaging and AI model evaluation. Understanding of evaluation metrics (e.g., accuracy, precision, recall) and model robustness. If you’re seeking a dynamic role where you can make an impact, we want to hear from you! At Leidos, we value innovation and strive to push boundaries in mission‑focused initiatives.