Data Science & Machine Learning Fellow

• 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., * 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.

• 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

• 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.

EEO StatementThe 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 these export controls may result in The Nuclear Company limiting its consideration of certain applicants.

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.