Spring 2027 AI Applied Research Internship
$52,000–$52,000 year
On-siteWashington, United States
Job Summary
Source and clean data to build, evaluate, and deploy machine learning and forecasting models for nuclear megaproject cost, schedule, and risk. Manage experiments and pipelines using MLflow, Kubeflow, or SageMaker, then own the full lifecycle including production monitoring, drift detection, and retraining triggers. Establish model validation to ensure results remain accurate and audit-ready. Work alongside full-time engineers to transform messy data into monitored services that support real reactor construction. This six-month co-op runs January to June 2027 with on-site days in Washington DC, offering housing and relocation support for out-of-area candidates.
Required Qualifications
- Currently enrolled in a BS or MS in Computer Science, Data Science, Statistics, Applied Math, or a related technical field
- Available for a full six-month term and returning to school afterward
- Strong Python and the ML data stack (pandas, NumPy, scikit-learn)
- Hands-on experience (coursework, research, projects, or an internship) building ML pipelines and evaluating models; comfortable approaching a time-series or forecasting problem from scratch
- U.S. Person status (U.S. citizen or lawful permanent resident)
- Willing and able to work on-site in Washington DC, five days a week, for the full six-month term
Desired Qualifications
- MLflow, Kubeflow, or SageMaker; drift detection and production monitoring
- Strong statistics, probability, and experimental design; SQL and relational data modeling
- Cloud data warehouses (Snowflake, Databricks, BigQuery); Palantir Foundry or AWS
- Interest in energy, national security, industrial software, or regulated industries
- Familiarity with nuclear engineering concepts
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.