Summer 2027 AI Applied Research Internship

The role

You will use reinforcement learning and optimization to solve problems regarding construction sequencing, capital allocation, and site security. You will formulate problems, build simulation or optimization models, and work with engineering to move models toward production.

What you will do

• Translate operational processes into well-defined modeling problems.
• Build environments that represent processes for model training and evaluation.
• Develop reinforcement learning, optimization, or forecasting models.
• Design rigorous experiments and keep reproducible codebases.
• Communicate results to technical and non-technical stakeholders.
• Work with engineering on how models are served, monitored, and updated in production.

Required qualifications

• Currently pursuing an MS or PhD in Computer Science, Machine Learning, Operations Research, Applied Math, Economics, Statistics, or a related quantitative field.
• Returning to MS or PhD program after the fellowship (expected graduation December 2027 or later).
• Production-quality Python and PyTorch.
• Solid machine learning fundamentals.
• Hands-on experience with at least one of: reinforcement learning, mathematical optimization, simulation and modeling, or time-series forecasting.
• Ability to translate a real-world process into a tractable formulation.
• Demonstrated ability to design, implement, and evaluate experiments with reproducible research practices.
• U.S. Person status (U.S. citizen or lawful permanent resident).
• Willing and able to work on-site in Washington DC, five days a week, for the full 12-week program.

Nice to have

• 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
• Full housing and relocation for fellows outside the DC metro area

Before you apply

• U.S. Person status (U.S. citizen or lawful permanent resident) is required.
• TNC does not provide visa sponsorship for these roles.
• Position requires access to information and technology subject to U.S. export controls.

About the company

The Nuclear Company is an AI tech-startup in the nuclear and energy space. They use an AI-first mindset to accelerate the deployment of nuclear energy through a design-once, build-many model.