The Nuclear Company
Spring 2027 AI/ML Engineering Intern
Washington, DC +1 · Internship · Posted Aug 7, 2026
- Location
- Washington, DC
Washington, District of Columbia, United States - Experience
- No specific number of years stated; coursework, personal projects, internships, or co-ops may demonstrate experience.
- Education
- Currently enrolled in a BS in Computer Science, Machine Learning, Data Science, Statistics, Mathematics, or a related technical field.
- Visa sponsorship
- U.S. Person status (U.S. citizen or lawful permanent resident) is required; TNC does not provide visa sponsorship for these roles.
About the job
Build and ship AI/ML features, agents, and data capabilities for the Nuclear Operating System used in nuclear reactor construction.
What you'll do
- Develop AI agents, workflows, predictive and anomaly-detection models, and integrations for NOS applications.
- Build data pipelines and contribute to NOS data ontology across project systems and operational data.
- Own model quality through evaluation criteria, regression suites, monitoring, and issue triage.
What they ask for
- Currently enrolled in a relevant bachelor's program; available for the full six-month co-op and returning to school afterward.
- Strong Python fundamentals and hands-on exposure to modern ML and LLM tooling through coursework or projects.
- Demonstrated experience shipping working software or ML projects.
- Able to work on-site in Washington, DC, five days a week; must be a U.S. Person (U.S. citizen or lawful permanent resident).
Nice to have
- Hands-on experience with LLMs, agents, retrieval-augmented generation, evaluations, or tool use.
- Experience with Palantir Foundry and AIP, AWS or another cloud platform; data analysis, MLOps, or CI/CD experience is also valued.
Pay
$25.00 an hour (base rate); $2,000 monthly (housing stipend)
Tools
Python, PyTorch, Hugging Face, MCP, Palantir Foundry, AWS GovCloud, MLflow, SageMaker
AI focus
Build LLM-driven workflows, retrieval-augmented generation, agents, predictive and anomaly-detection models, and production evaluation and monitoring for AI features.
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