Machine Learning Systems Research Intern at Red Hat working on AI inference and model optimization techniques. Collaborating with experts in the field while gaining hands - on experience in applied ML research.
About the role
- Junior AI/ML Engineer supporting data preprocessing and model evaluation at Node.Digital. Collaborate on AI/Machine Learning efforts with government and commercial clients.
Responsibilities
- Support data preprocessing and feature engineering pipelines under senior engineer direction: clean, normalize, and validate HRSA fraud-related datasets; handle class imbalance preparation (SMOTE, undersampling) and train/validation/test split management.
- Assist in the development, training, and evaluation of supervised fraud classification models; compute and document standard evaluation metrics (accuracy, precision, recall, F1 score, AUC-ROC, confusion matrices) for government review in EPLC-required model evaluation reports.
- Maintain and monitor ML experiment tracking using MLflow or equivalent tooling approved for the IRMS environment; log hyperparameter configurations, training runs, and evaluation results with full reproducibility documentation.
- Support model drift detection and retraining pipelines: run scheduled evaluation jobs, flag performance degradation against established baselines, and escalate findings to the AI/ML Lead Engineer and Fraud AI/ML SME.
- Assist the NLP/NER pipeline team (Rohit) with data transformation tasks: format-convert NER pipeline outputs into feature-compatible schemas for downstream ML models; validate entity extraction quality against labeled reference sets.
- Develop and maintain Jupyter notebook-based model exploration and reporting artifacts for use in EPLC deliverables, sprint reviews, and government demonstrations.
- Support UiPath Maestro agent integration testing: prepare model inference payloads, validate agent input/output schemas, and assist with integration testing between ML model inference APIs and the persona-based agent layer.
- Implement and maintain data pipeline scripts (Python/Pandas/NumPy) for batch data ingestion, feature store updates, and model scoring batch runs within the IRMS security boundary.
- Follow and enforce IRMS boundary data handling procedures: ensure no PII/PHI is processed outside approved environments; maintain developer/test environment segregation per HHS security policy.
- Produce supporting artifacts for EPLC deliverables: training data specifications, model evaluation appendices, data dictionary updates, and sprint retrospective documentation as directed by the PM and AI/ML Lead.
- Participate in code reviews; adhere to OWASP secure coding standards, NIST SP 800-160 engineering principles, and Node’s internal CI/CD quality gates.
Requirements
- Bachelor’s degree in Computer Science, Data Science, Mathematics, Statistics, or a closely related field; recent graduates with strong applied ML coursework or project portfolios will be considered.
- 1–3 years of hands-on experience (including internships, graduate research, or project work) in machine learning, data science, or data engineering with Python.
- Proficiency in Python ML stack: scikit-learn, Pandas, NumPy; familiarity with at least one deep learning framework (TensorFlow or PyTorch) for model evaluation and inference tasks.
- Demonstrated experience with standard ML evaluation workflows: train/validation/test split design, cross-validation, metric computation, and results documentation.
- Experience with Jupyter notebooks for data exploration, model evaluation, and technical reporting.
- Familiarity with Git-based version control and CI/CD principles; ability to work within a structured sprint cadence with documented deliverable commitments.
- Demonstrated ability to handle sensitive data responsibly; understanding of data governance, access control, and the importance of environment segregation in a regulated or government setting.
- Strong written communication skills: ability to produce clear, organized technical documentation suitable for government review.
Benefits
- Medical
- Dental
- Vision
- Basic Life
- Health Saving Account
- 401K Matching
- Three weeks of PTO/Sick
- 11 Paid Holidays
- Pre-Approved Online Training
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
Senior Machine Learning Engineer focused on Machine Learning Ops for Autodesk software products. Building production infrastructures, ensuring AI - powered experiences, and collaborating with cross - functional teams.
Senior Machine Learning Engineer developing and deploying machine learning models for autonomous trucks. Collaborating with various teams to enhance safe and efficient decision - making in freight environments.
AI Engineer at PayPal designing, building, and deploying autonomous AI systems powered by LLMs. Collaborating across teams on AI engineering, distributed systems, and product development.
Machine Learning Engineer designing and deploying advanced training capabilities to support U.S. Navy operational readiness. Collaborate on machine - learning models to enhance combat system training environments.
Cloud MLOps Engineer supporting Data Science and Engineering teams by automating CI/CD pipelines and managing multi - cloud infrastructure for ML production.
Lead development of Agentic AI capabilities and LLM applications for multiple mission management applications. Mentor teams to implement ML algorithms addressing customer challenges.
Staff AI/ML Engineer at CACI responsible for developing AI/ML algorithms and analyzing datasets. Join a high - performing team supporting national safety missions.
AI/ML Engineer at CACI developing machine learning algorithms for multiple applications. Collaborating with a research team to implement cutting - edge AI/ML solutions for customer missions.
Senior Computer Vision AI/ML Engineer leading a team in AI/ML algorithm implementation for remote sensing solutions. Responsibilities include training models and analyzing datasets with a focus on defense and commercial applications.