The Nuclear Company
Research Scientist
Washington, DC
Sponsorship not specified$150k-$173kDetected 9 days ago
PythonAlgorithmsMachine LearningDeep LearningPyTorchNLPCybersecuritySOC OperationsDesign SystemsProject ManagementStakeholder ManagementAccountingSupply ChainCustomer SupportMechanical DesignControlsUnityResearchExperimental DesignLeadershipCommunicationCollaborationMentoring
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.
- 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.
- 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
Responsibilities
- 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.
- 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.
- 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 Intelligence: Build models for alert prioritization, anomaly detection, and patrol scheduling that support physical and cyber security operations across a distributed multi-site infrastructure.
- 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.
- 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.
- 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.
Requirements
- Experience formally mentoring PhD-level researchers or interns
- Some of the exciting topics you are likely to work on include:
Nice to have
- 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
Compensation
- Competitive compensation packages
Benefits
- Medical, dental, vision plans
- Generous vacation policy, plus holidays
- Software Engineering: Production-quality Python; deep learning frameworks (PyTorch); version control, testing, and reproducibility practices expected of research code that ships into production systems.
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