Research Scientist

The role

Deploying a fleet of nuclear power plants is one of the most ambitious and consequential undertakings in the global energy transition — and The Nuclear Company is doing it. You will join a small but world-class Applied Research and AI team and work on genuinely hard, open research problems at the intersection of AI and large-scale infrastructure: how do you optimize construction across a fleet of simultaneous sites, allocate capital intelligently under deep uncertainty, and keep a distributed critical infrastructure secure?

These are not incremental problems — they sit at the frontier of applied AI research, with real operational stakes and the potential to reshape how the energy industry is built.

In this role, you will research novel approaches to these problems, collaborate closely with domain experts and engineering partners, and see your work through to deployed systems that actively inform decisions — across construction, capital planning, security operations, and beyond. You will work alongside some of the top nuclear industry experts in the field, a team of PhD-level researchers, and software engineers dedicated to bringing your models into production — giving you the domain depth, technical support, and collaborative environment to do your best work.

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Research Scientist

The Nuclear Company, Washington, DC +1

The role

These are not incremental problems — they sit at the frontier of applied AI research, with real operational stakes and the potential to reshape how the energy industry is built.

What you will do

Research & Modeling

Problem Formulation: Translate complex operational processes into well-defined research problems; identify the right modeling approach for each domain and build the case for why it will work in practice.
Simulation & Evaluation: Build simulation environments that faithfully represent our operational processes — construction scheduling, portfolio sequencing, security operations — and can be used to train, evaluate, and iterate on decision-making models.
Empirical Research: Design rigorous experiments, maintain reproducible codebases, and communicate results clearly in internal reports and, where the research warrants it, external publications.

Some of the exciting topics you are likely to work on include:

Construction Schedule Optimization

Schedule Optimization: Develop models that optimize construction scheduling across multiple concurrent sites — minimizing schedule variance, resource idle time, and cascading delays across a growing fleet of projects.
Dynamic Rescheduling: Design approaches that adapt scheduling decisions in real time to disruptions — supply chain delays, labor fluctuations, permitting hold-ups — learning from historical project data to improve over time.

Site Portfolio Optimization

Portfolio Decision Systems: Build models that inform how we sequence site development and allocate capital across a growing fleet — accounting for regulatory milestones, capital constraints, and correlated risks across sites.
Uncertainty Quantification: Develop approaches that account for uncertainty in key inputs — permitting timelines, cost distributions, grid demand forecasts — to produce portfolio decisions with bounded downside.

Security Operations

Security Intelligence: Build models for alert prioritization, anomaly detection, and patrol scheduling that support physical and cyber security operations across a distributed multi-site infrastructure.
Human-in-the-Loop Design: Design systems where models and human analysts share decision authority appropriately — communicating uncertainty clearly and degrading safely when operating outside familiar conditions.

Production Deployment & Cross-Functional Collaboration

Model Deployment: Collaborate with engineering to define how models are served, monitored, updated, and overridden in production — ensuring deployed systems are reliable, maintainable, and trusted by the teams that use them.
Stakeholder Communication: Present research results and system performance to operations, security, and leadership stakeholders; translate findings into actionable operational recommendations.

What you need

Research Foundation: PhD in Computer Science, Machine Learning, Operations Research, Economics, Applied Mathematics, or a closely related quantitative field — or MS with a demonstrable track record of independent research output (publications, patents, or equivalent deployed systems).
Reinforcement Learning Depth: Hands-on experience implementing and evaluating deep RL algorithms; fluency in policy gradient methods (PPO, TRPO, SAC), value-based approaches (DQN variants, IQL), and the tradeoffs between model-free and model-based RL.
Simulation Engineering: Experience building RL training environments; demonstrated ability to translate complex real-world operational processes into tractable MDP formulations with appropriate state/action/reward design.
Software Engineering: Production-quality Python; deep learning frameworks (PyTorch); version control, testing, and reproducibility practices expected of research code that ships into production systems.
Startup Agility: A demonstrated ability to operate in a fast-moving environment where problem definitions evolve, priorities shift, and hands-on technical contribution — not just research direction — is expected at all levels.

Preferred Experience

Offline / Batch RL: IQL, CQL, TD3+BC, Decision Transformer, or similar methods — directly relevant given limited online interaction in our deployment environments.
Combinatorial Optimization + ML: Graph neural networks for scheduling or routing (GCN, attention-based), neural combinatorial optimization, or hybrid learned/exact solver approaches.
Multi-Agent RL: MADDPG, QMIX, MAPPO, or related methods for multi-site coordination and adversarial security formulations.
Stochastic / Robust Optimization: CVaR-constrained RL, distributionally robust MDPs, or chance-constrained programming for decision-making under uncertainty.
Production RL Deployment: Experience monitoring and retraining RL systems post-deployment, including distribution shift detection and safe policy update procedures.
Domain Exposure: Construction project management, infrastructure operations, energy industry, electricity markets, nuclear power, industrial control systems, or physical/cyber security for critical infrastructure.
Game Theory: Familiarity with game-theoretic frameworks — strategic interactions, mechanism design, adversarial dynamics, or equilibrium concepts — provides useful modeling perspective for problems where multiple decision-makers interact, compete, or coordinate.
Behavioral Science: An understanding of how people actually make decisions — including cognitive biases, bounded rationality, and responses to uncertainty — is valuable when building systems that work alongside human decision-makers.
Research Program Leadership: 2-5 years of post-PhD research experience with demonstrated ownership of a research program: defining scope, setting methodology under uncertainty, and being accountable for both published results and deployed system quality.
Publication Track Record: Publications at top-tier venues (NeurIPS, ICML, ICLR, AAMAS, or equivalent); experience driving a team’s external research agenda and identifying which contributions warrant external dissemination.
Mentorship & Team Development: Experience formally mentoring PhD-level researchers or interns; ability to define research objectives for others and provide substantive technical guidance on experimental design and problem formulation.
Cross-Functional Technical Leadership: Experience making research architecture decisions that span research and engineering — model serving, retraining triggers, monitoring design — and communicating technical direction to non-technical senior stakeholders.

Research Scientist

The role

Research Scientist

The role

What you will do

What you need

Pay and benefits

About The Nuclear Company

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