Research Scientist

The Nuclear Company is the fastest growing AI tech-enabled 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— Transparency, 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

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. This is a place to make major contributions on a small but growing team, develop your skills across a remarkable range of hard problems, and be part of something that genuinely matters.

Responsibilities

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.

Experience

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: Fa