Spring 2027 AI Applied Research Internship

Spring 2027 Ai Applied Research Internship

The Nuclear Company is the fastest growing AI tech-startup in the nuclear and energy space, pioneering a fleet-scale approach to building the next generation of nuclear reactors. Through our design-once, build-many model, we're accelerating the deployment of safe, reliable, and affordable nuclear energy.

We operate with an AI-first mindset. Every employee is expected to leverage AI, technology, and the Nuclear Operating System (NOS) as integral components of their role to improve the quality, speed, and impact of their work. We expect every team member to continuously identify opportunities to automate workflows, enhance decision-making, improve processes, and contribute to the ongoing evolution of NOS as a strategic operating capability that enables The Nuclear Company to scale with excellence.

We hire people who are driven by purpose, thrive in ambiguity, and are energized by building what has never been built before. Our team combines intellectual curiosity with high agency, embraces candid feedback and continuous learning, and holds themselves and others to exceptional standards. Our values— Trust, Responsibility, Unity, Scrappiness, and Tenacity —guide how we hire, collaborate, and make decisions every day.

They are not words on a wall; they are the standard by which we operate. Trust is the foundation of our safety culture, fostering intellectual honesty, accountability, and open communication, while our values challenge every team member to execute with urgency, humility, resilience, and an unwavering commitment to our mission.

The United States is building nuclear power again, and The Nuclear Company is writing the software that makes it possible. NOS, the Nuclear Operating System, runs the regulatory, supply chain, cost, schedule, and field work behind real reactor construction. The work matters for national security, energy independence, and the climate.

You will take models from raw data to production on problems that matter to a real build. As an AI/ML Intern, you work with data-scientist depth: build machine learning and forecasting models, deploy them, and own the full lifecycle, including pipelines, evaluation, and production monitoring for drift. You work next to full-time engineers and own a model end to end, from messy data to a running, monitored service that people rely on.

This is a six-month co-op available in Spring 2027 (January to June), aligned to the academic calendar. Base location is Washington DC, on-site five days a week, with full housing and relocation for co-ops outside the DC metro area.

• 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 Sage Maker.
• Build forecasting and time-series models for mega project 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.

• 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, Num Py, 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.
• 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.

• MLflow, Kubeflow, or Sage Maker; drift detection and production monitoring.
• Strong statistics, probability, and experimental design; SQL and relational data modeling.
• Cloud data warehouses (Snowflake,…