Summer 2027 AI Applied Research Internship

About the role

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.

Responsibilities

• 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.
• Demonstrated ability to design, implement, and evaluate experiments, with reproducible research practices (version control, testing).
• 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 12-week program

Preferred Experience

• Deep RL: policy gradient (PPO, SAC) or value-based (DQN, IQL) methods; offline / batch RL (CQL, IQL, TD3+BC, Decision Transformer).
• Combinatorial optimization with ML: graph neural networks for scheduling or routing, or neural combinatorial optimization.
• Multi-agent RL (MAPPO, QMIX) or stochastic / robust optimization (CVaR-constrained, chance-constrained, distributionally robust).
• Uncertainty quantification; a game-theory or behavioral-science perspective on decision-making.
• MLOps for models in production: serving, monitoring, retraining, and distribution-shift detection.
• Domain exposure: construction or infrastructure operations, energy or electricity markets, industrial control systems, or critical-infrastructure security.

Benefits

• Competitive compensation packages
• 401k with company match
• Medical, dental, vision plans
• Generous vacation policy, plus holidays

Estimated Starting Salary Range

The estimated starting rate for this role is $25.00 an hour plus a $2,000 monthly housing stipend less applicable withholdings and deductions, paid on a bi-weekly basis. The actual pay offered may vary based on relevant factors as determined in the Company's discretion, which may include experience, qualifications, tenure, skill set, availability of qualified candidates, geographic location, certifications held, and other criteria deemed pertinent to the particular role.