Summer 2027 AI Applied Research Internship

Summer 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, at a scale not attempted in a generation, and The Nuclear Company is leading it. Our Applied Research and AI team works on the open problems that decide how a fleet of plants gets built: sequencing construction across many concurrent sites, allocating capital under deep uncertainty, and keeping a distributed critical infrastructure secure.

These are hard problems with real operational stakes, and the work ships into systems that inform real decisions.

You will put reinforcement learning and optimization to work on problems that decide how a fleet of plants gets built: how to sequence construction across many sites, where to place capital under uncertainty, and how to keep a distributed site secure. As a Data Science & Machine Learning Fellow, you formulate the problem, build a simulation or optimization model, evaluate it rigorously, and help move it toward a deployed decision system.

You work alongside nuclear industry experts to deliver solutions that inform real decisions and create business value.

This is a 12-week Summer 2027 fellowship (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 fellows outside the DC metro area.

• Problem formulation: translate operational processes (construction scheduling, portfolio sequencing, security operations) into well-defined modeling problems and make the case for the right approach.
• Simulation and evaluation: build environments that faithfully represent these processes so models can be trained, evaluated, and iterated on.
• Modeling: develop reinforcement learning, optimization, or forecasting models for schedule optimization, capital allocation under uncertainty, or anomaly detection and alert prioritization.
• Empirical research: design rigorous experiments, keep reproducible codebases, and communicate results clearly to technical and non-technical stakeholders.
• Production path: work with engineering on how models are served, monitored, updated, and safely overridden in production.

• Currently pursuing an MS or PhD in Computer Science, Machine Learning, Operations Research, Applied Math, Economics, Statistics, or a related quantitative field. Returning to your MS or PhD program after the fellowship (expected graduation December 2027 or later).
• Production-quality Python and PyTorch, with solid machine learning fundamentals.
• Hands-on experience (coursework, research, or projects) with at least one of:…