Summer 2027 AI Applied Research Internship

The Nuclear Company; Washington (Washington County); On-site; USD 71,635,000 - 104,698,000; Full time

Benefits offered by this job

Competitive compensation

401k with company match

Medical, dental, vision plans

Generous vacation & holidays

Job summary

The Nuclear Company in Washington DC is launching a 12-week Summer 2027 Data Science & MachineLearning Fellow program. You will define modeling problems for fleet-scale nuclear deployment, build simulations, and evaluate models with experts to inform real decisions and business value.

You will work with an Applied Research and AI team to translate complex operations into tractable ML solutions, with on-site housing and relocation support for fellows.

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 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 with reinforcement learning, mathematical optimization, simulation and modeling, or time‑series forecasting.
• Able to translate real-world processes into tractable formulations (MDP or optimization model) and explain the modeling choice.
• Experience designing, implementing, and evaluating experiments with reproducible research practices.

Responsibilities

• Problem formulation: translate operational processes into modeling problems and justify the approach.
• Simulation and evaluation: build environments for training, evaluating, and iterating models.
• Modeling: develop RL, optimization, or forecasting models for schedule optimization and capital allocation under uncertainty.
• Empirical research: design experiments, maintain reproducible code, and communicate results to stakeholders.
• Production path: collaborate with engineering on serving, monitoring, updating, and safe override of models.

Skills

Python

PyTorch

Reinforcement learning

Optimization

Experiment design

Education

MS or PhD

Computer Science

Machine Learning

Operations Research

Applied Math

Economics

Statistics

Tools

Git

Job description

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.

About the role

The United States is building nuclear power again, at a scale notattemptedin 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,allocatingcapital 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 aData Science & MachineLearningFellow, you formulate the problem, build a simulation or optimization model, evaluate it rigorously, and help move it toward a deployed decision system. You workalongsidenuclearindustryexpertsto deliver solutions that inform real decisions and create business value.

This is a 12-week Summer 2027fellowship(May to August), aligned to the academic calendar. Base location is Washington DC, on‑site five days a week, with full housing and relocation forfellowsoutside the DC metro area.

Responsibilities

• Problem formulation:translateoperational processes (construction scheduling, portfolio sequencing, security operations) into well‑defined modeling problems andmakethe case for the right approach.
• Simulation and evaluation: build environments that faithfully represent theseprocessesso 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 thefellowship(expected graduation December 2027 or later).
• Production‑quality Python andPyTorch, 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) isrequired, 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.
• MLOpsfor models in production: serving, monitoring, retraining, and distribution‑shift detection.
• Domain exposure: construction or infrastructure operations, energy or electricity markets, industrial control systems, orcritical‑infrastructuresecurity.

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, certifications held, and other criteria deemed pertinent to the particular role.

EEO Statement

The Nuclear Company is an equal opportunity employer committed to fostering an environment of inclusion in the workplace. We provide equal employment opportunities to all qualified applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We prohibit discrimination in all aspects of employment, including hiring, promotion, demotion, transfer, compensation, and termination.

Export Control

Certain positions at The Nuclear Company may involve access to information and technology subject to export controls under U.S. law. Compliance with the export controls may result in The Nuclear Company limiting its consideration of certain applicants.