Spring 2027 AI Applied Research Internship

The Nuclear Company•Washington, DC

4h•Onsite

About The Position

The Nuclear Company is a rapidly growing AI tech startup focused on the nuclear and energy sectors, aiming to build the next generation of nuclear reactors using a scalable, design-once, build-many approach. The company emphasizes an AI-first mindset, expecting all employees to utilize AI, technology, and the Nuclear Operating System (NOS) to enhance their work. The role involves contributing to the automation of workflows, improvement of decision-making and processes, and the evolution of NOS as a core strategic capability. The company seeks individuals who are purpose-driven, comfortable with ambiguity, and excited by innovation. Team members are expected to possess intellectual curiosity, high agency, embrace feedback, and maintain high standards, guided by core values of Trust, Responsibility, Unity, Scrappiness, and Tenacity.

Requirements

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) is required.
Willing and able to work on-site in Washington DC, five days a week, for the full six-month term.

Nice To Haves

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 (not required, we teach the domain)

Responsibilities

Take models from raw data to production: source, clean, and explore data, then build, evaluate, and deploy.
Build ML pipelines and manage experiments and models with MLflow, Kubeflow, or SageMaker.
Build forecasting and time-series models for megaproject cost, schedule, and risk.
Own the full lifecycle and production monitoring, including drift detection and retraining triggers.
Establish model evaluation and validation so results stay accurate and audit-ready.