Summer 2027 AI​/ML Engineering Intern

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 build the AI that people use to build power plants.

As an AI/ML Engineer Intern on the Platform Integration & AI/Data squad, you take real scope on the machine learning and agent capabilities inside NOS: LLM-driven workflows, retrieval-augmented generation, and agents that operate against NOS data and tools, plus the evals and monitoring that keep AI features safe in production. You work next to full-time engineers and nuclear domain experts, ship real models and features to production, and see your work put to use by the teams building plants.

This is a 12-week Summer 2027 internship (May to August), 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.

• 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 Gov Cloud, 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.

• 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). We care about what you've built, not just what you've studied.
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