Junior AI/ML Engineer

Role Overview

Node.Digital LLC is seeking a Junior AI/ML Engineer to support AI automation and machine learning initiatives for enterprise clients in both the government and commercial sectors. This role is based in Herndon, VA and follows a hybrid work arrangement.

The position is best suited to someone with a solid foundation in Python-based machine learning, data preparation, model evaluation, and technical documentation, especially in regulated environments where data handling and reproducibility are critical.

Preferred: US citizenship.

Key Responsibilities

• Prepare and refine HRSA fraud-related datasets by cleaning, normalizing, validating, and organizing data for training workflows, including split management and class imbalance techniques such as SMOTE and undersampling.
• Help build, train, and assess supervised fraud detection models, and document standard performance measures such as accuracy, precision, recall, F1 score, AUC-ROC, and confusion matrices for government-facing review materials.
• Track experiments using MLflow or an approved equivalent inside the IRMS environment, capturing hyperparameters, run details, and results in a reproducible format.
• Assist with drift monitoring and retraining pipelines by running scheduled checks, identifying degradation versus baseline performance, and escalating issues to the AI/ML Lead Engineer and Fraud AI/ML SME.
• Support the NLP/NER workflow by converting pipeline outputs into schema-ready features and checking entity extraction results against labeled reference data.
• Create and maintain Jupyter notebook assets used for model exploration, reporting, sprint reviews, EPLC deliverables, and government demonstrations.
• Help test UiPath Maestro agent integrations by preparing inference payloads, validating input/output schemas, and supporting end-to-end integration checks between model APIs and the persona-based agent layer.
• Write and maintain Python, Pandas, and NumPy scripts for batch ingestion, feature-store updates, and batch scoring within the IRMS security boundary.
• Follow IRMS data-handling rules by keeping PII and PHI inside approved environments and maintaining strict separation between development and test environments in line with HHS policy.
• Prepare supporting documentation for EPLC deliverables, including training data specs, model evaluation appendices, data dictionary updates, and sprint retrospective notes as assigned by the PM and AI/ML Lead.
• Take part in code reviews and comply with OWASP secure coding practices, NIST SP 800-160 principles, and Node.Digital’s internal CI/CD quality requirements.

Requirements

• A bachelor’s degree in Computer Science, Data Science, Mathematics, Statistics, or a related discipline; candidates who recently graduated and can show strong applied ML coursework or project work may also be considered.
• 1 to 3 years of practical exposure to machine learning, data science, or data engineering using Python, including internships, graduate research, or project-based experience.
• Hands-on ability with the Python ML stack, especially scikit-learn, Pandas, and NumPy, plus familiarity with TensorFlow or PyTorch for evaluation and inference tasks.
• Working knowledge of standard ML evaluation methods such as train/validation/test splits, cross-validation, metric calculation, and results documentation.
• Experience using Jupyter notebooks for data analysis, model evaluation, and technical reporting.
• Comfort working with Git-based version control and CI/CD concepts in a structured sprint environment with deliverable commitments.
• Ability to handle sensitive data responsibly and understand data governance, access controls, and environment segregation in regulated or government settings.
• Strong written communication skills with the ability to produce clear technical documents for government review.

Benefits

• Medical coverage
• Dental coverage
• Vision coverage
• Basic life insurance
• Health Saving Account
• 401(k) matching
• Three weeks of PTO/sick leave
• 11 paid holidays
• Pre-approved online training

Additional Information

This role is tied to a hybrid setup in Herndon, VA. The company focuses on modern cloud, mobile, and AI/ML solutions for digital transformation across enterprise, government, and commercial environments. The work emphasizes reproducible experimentation, secure handling of regulated data, and production support for fraud-related machine learning initiatives.

Eligibility

Applicants should hold or be pursuing the background described above and should be able to work in a setting that may require US citizenship. The role is suitable for recent graduates with strong machine learning portfolios as well as candidates with up to three years of relevant experience.