Applied Machine Learning Engineer (NLP/AI)

Leidos•Bethesda, MD

1d•Hybrid

About The Position

The Government Health and Safety Solutions Operation is seeking an Applied Machine Learning Engineer. This position requires being ONSITE IN BETHESDA, MD (with some remote opportunity). Responsibilities include designing, developing, and maintaining AI/ML solutions to support NIH grant application intake, peer review workflows, and analytics. This role involves developing and applying NLP and machine learning techniques for tasks such as reviewer–application matching, keyword extraction, and document analysis. The engineer will build, evaluate, and iteratively improve machine learning models using structured and unstructured data, including text, documents, and images. They will design and implement end-to-end ML pipelines, including data ingestion, preprocessing, feature/embedding generation, model execution, evaluation, and output generation. Debugging, testing, and optimizing ML pipelines and tools to ensure reliable, consistent, and reproducible results are key. Refactoring and improving code for performance, scalability, and maintainability is also expected. The role involves working with large and complex datasets, implementing data validation, quality checks, and preprocessing workflows. Conducting experiments to evaluate model performance, analyze results, and refine approaches based on quantitative and qualitative findings is crucial. Collaboration with cross-functional teams to translate business needs into practical AI/ML solutions and ensuring reproducibility and transparency through documentation, versioning, and structured workflows are essential. Communicating methods, results, and limitations clearly to both technical and non-technical stakeholders is required. Staying current with advancements in applied AI/ML, including NLP, embeddings, and generative AI, and evaluating their applicability to NIH use cases is also part of the role.

Requirements

Master’s in data science, Computer Science, Computational Linguistics, or related field (or equivalent experience).
Minimum of 3–5 years of relevant experience in applied machine learning, data science, or a related field.
Strong programming expertise in Python (preferred) and/or R.
Demonstrated experience delivering end-to-end ML solutions, including model development, evaluation, and pipeline implementation.
Hands-on experience developing and applying machine learning models and workflows.
Experience with NLP techniques such as text classification, embeddings, semantic similarity, or related methods.
Experience working in cloud or shared compute environments (e.g., Azure, Biowulf, or similar).
Experience building and maintaining data processing or ML pipelines.
Experience working with real-world datasets, including data cleaning, preprocessing, and feature engineering.
Ability to debug, test, and improve complex code and analytical workflows.
Familiarity with at least one modern ML framework (e.g., PyTorch, TensorFlow, scikit-learn, or equivalent).
Strong analytical and problem-solving skills, including the ability to evaluate model performance and interpret results.
Excellent communication skills, with the ability to explain technical concepts to diverse audiences.

Nice To Haves

Experience with transformer-based models or large language models (LLMs), including practical applications such as text analysis or document processing.
Experience with reviewer matching, recommendation systems, or document similarity problems.
Familiarity with distributed data processing tools (e.g., Spark, Dask, Ray).
Experience with experiment tracking, model versioning, or reproducible workflows (e.g., MLflow or similar tools).
Familiarity with NIH data systems, biomedical text, or scientific research data.
Experience with medical or scientific imaging, including development or evaluation of models for detecting altered, manipulated, or AI-generated images.
Understanding of evaluation metrics (e.g., accuracy, precision/recall) and model robustness.

Responsibilities

Design, develop, and maintain AI/ML solutions to support NIH grant application intake, peer review workflows, and analytics.
Develop and apply NLP and machine learning techniques (e.g., embeddings, classification, clustering, similarity analysis) for tasks such as reviewer–application matching, keyword extraction, and document analysis.
Build, evaluate, and iteratively improve machine learning models using structured and unstructured data, including text, documents, and images where applicable.
Design and implement end-to-end ML pipelines, including data ingestion, preprocessing, feature/embedding generation, model execution, evaluation, and output generation.
Debug, test, and optimize ML pipelines and tools to ensure reliable, consistent, and reproducible results.
Refactor and improve code to enhance performance, scalability, and maintainability.
Work with large and complex datasets, including implementing data validation, quality checks, and preprocessing workflows.
Conduct experiments to evaluate model performance, analyze results, and refine approaches based on quantitative and qualitative findings.
Collaborate with cross-functional teams (data scientists, analysts, program staff, and engineers) to translate business needs into practical AI/ML solutions.
Ensure reproducibility and transparency through documentation, versioning, and structured workflows.
Communicate methods, results, and limitations clearly to both technical and non-technical stakeholders.
Stay current with advancements in applied AI/ML, including NLP, embeddings, and generative AI, and evaluate their applicability to NIH use cases.