Fall 2026 AI/ML Engineering Intern

The Nuclear Company•Washington, DC

19h•Onsite

About The Position

The Nuclear Company is seeking an AI/ML Engineering Intern for Fall 2026 to contribute to the development of the Nuclear Operating System (NOS). This role involves building AI and machine learning features for NOS applications, including LLM-driven workflows, retrieval-augmented generation, and agents that interact with NOS data and tools. The intern will work alongside full-time engineers and domain experts, ship production-ready models and features, and see their work applied to real reactor construction projects. This is a six-month co-op position available from August to December 2026, based in Washington DC, with on-site work required five days a week. Full housing and relocation assistance are provided for interns outside the DC metro area.

Requirements

Currently enrolled in a BS in Computer Science, Machine Learning, Data Science, Statistics, Mathematics, or a related technical field.
Available for a full six-month term and returning to school afterward.
Strong Python fundamentals and hands-on exposure to modern ML and LLM tooling (PyTorch, Hugging Face, or similar) through coursework or projects.
A track record of shipping working software or ML projects (course projects, personal projects, or prior internships or co-ops).
This position requires access to information and technology subject to U.S. export controls (including DOE 10 CFR Part 810 and NRC requirements). U.S. Person status (U.S. citizen or lawful permanent resident) is required, and TNC does not provide visa sponsorship for these roles.
Willing and able to work on-site in Washington DC, five days a week, for the full six-month term.

Nice To Haves

Hands-on experience with LLMs and agents: prompting, fine-tuning, retrieval-augmented generation, evals, or tool use (MCP or similar frameworks).
Coursework or project experience with Palantir Foundry and AIP, AWS, or another major cloud platform — Foundry exposure is heavily weighted.
Solid data fundamentals: comfortable doing exploratory data analysis and working with large, messy datasets, including time-series or sensor data.
Exposure to MLOps tooling (MLflow, SageMaker, Vertex AI) or CI/CD for models.
Interest in energy, national security, industrial software, or regulated industries.
Familiarity with nuclear engineering concepts (not required, we teach the domain).

Responsibilities

Ship ML and AI features inside NOS applications teams use every day: site evaluation, red flag analysis, scheduling, lessons learned, and the stakeholder and project tools running across active TNC projects.
Build AI agents and agent workflows that operate against NOS data and tools, including the MCP interfaces and tool-use frameworks they depend on.
Develop data pipelines and integrations across Palantir Foundry, AWS GovCloud, and more — and contribute to the data ontology so models and agents can use NOS data reliably.
Build predictive and anomaly-detection models that support operations and engineering decisions, including time-series analysis of sensor and operational data from active projects.
Help own model quality end-to-end: eval criteria, acceptance thresholds, and regression suites that keep NOS audit-ready and results reproducible; triage issues and root-cause failures.