The Nuclear Company
Spring 2027 AI Applied Research Internship
Washington, DC +1 · Internship · Posted Aug 14, 2026
- Location
- Washington, DC
Washington, District of Columbia, United States - Experience
- No years specified; hands-on experience may come from coursework, research, projects, or an internship.
- Education
- Currently enrolled in a BS or MS in a specified or related technical field; must return to school afterward.
- Visa sponsorship
- U.S. Person status (U.S. citizen or lawful permanent resident) is required; no visa sponsorship.
About the job
Build, deploy, and monitor machine-learning and forecasting models for nuclear construction projects, owning their lifecycle from raw data through production.
What you'll do
- Source, clean, and explore data; build, evaluate, and deploy models.
- Develop ML pipelines and forecasting/time-series models for project cost, schedule, and risk.
- Monitor production models, detect drift, trigger retraining, and establish audit-ready evaluation and validation.
What they ask for
- Currently enrolled in a BS or MS in Computer Science, Data Science, Statistics, Applied Math, or a related technical field; available for the full six-month term and returning to school afterward.
- Strong Python and ML data-stack skills, including pandas, NumPy, and scikit-learn.
- Hands-on experience building ML pipelines and evaluating models; able to approach a forecasting problem from scratch.
- U.S. Person status (U.S. citizen or lawful permanent resident); able and willing to work on-site in Washington, DC, five days a week for the full term.
Nice to have
- MLflow, Kubeflow, or SageMaker; drift detection and production monitoring.
- Statistics, probability, experimental design, SQL, relational data modeling, cloud data warehouses, Palantir Foundry, AWS, or familiarity with nuclear engineering concepts.
Pay
$25.00 an hour (base rate); $2,000 monthly housing stipend (stipend)
Tools
Python, pandas, NumPy, scikit-learn, MLflow, Kubeflow, SageMaker, SQL
AI focus
Build and productionize ML pipelines, forecasting and time-series models; evaluate, monitor for drift, and manage retraining.
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