Hiring: Research Scientist Company: The Nuclear Company Location: Washington, DC Job Posted Time: 2026-09-16 12:22:54 Employment Type: Hybrid Target Skills & Keywords : Deep Learning, Design System, Machine Learning, PyTorch, Python, Reinforcement Learning About the job Experience: •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. •Offline / Batch RL: IQL, CQL, TD3+BC, Decision Transformer, or similar methods — directly relevant given limited online interaction in our deployment environments. Required Skills: •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. •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. •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. Qualifications: •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). Compensation: •$150,000 - $173,000 / year Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!