Research Scientist

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.

Requirements

• 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 gr

Additional Information

The Nuclear Company is the fastest growing startup in the nuclear and energy space creating a never before seen fleet-scale approach to building nuclear reactors. Through its design-once, build-many approach and coalition building across communities, regulators, and financial stakeholders, The Nuclear Company is committed to delivering safe and reliable electricity at the lowest cost, while catalyzing the nuclear industry toward rapid development in America and globally.

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