Applied Machine Learning Engineer

About

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, TensorFlow, 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.