Research Scientist

• 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.

• 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.

• 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:

• 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.
• 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.
• Stakeholder Communication: Present research results and system performance to operations, security, and leadership stakeholders; translate findings into actionable operational recommendations.

• Competitive compensation packages
• 401k with company match
• Medical, dental, vision plans
• Generous vacation policy, plus holidays

Estimated Starting Salary Range The estimated starting salary range for this role is $150,000 - $173,000 annually less applicable withholdings and deductions, paid on a bi-weekly basis. The actual salary offered may vary based on relevant factors as determined in the Company’s discretion, which may include experience, qualifications, tenure, skill set, availability of qualified candidates, geographic location, certifications held, and other criteria deemed pertinent to the particular role.

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.