Responsibilities

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.
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.
Required Experience
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: reinforcement learning, mathematical optimization, simulation and modeling, or time-series forecasting.
Able to translate a messy real-world process into a tractable formulation (an MDP with sensible state, action, and reward, or an optimization model) and explain the modeling choice. Running pre-built models on clean benchmarks is not enough.

Summer 2027 AI Applied Research Internship at Thenuclearcompany · LumiJobs

Summer 2027 AI Applied Research Internship

Responsibilities

Qualification

Preferred

Benefits

Company

Thenuclearcompany

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