AI/ML Engineer

 

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

The Nuclear Company is hiring an AI Engineer to ship the machine learning and agent capabilities inside NOS, the software platform behind one of the largest nuclear buildouts in the United States. You’ll work in the Platform Integration & AI/Data squad and report to the Director of Platform Integrations. 

This is a hands-on engineering role. You’ll embed ML models and LLM-driven workflows directly into the NOS applications our internal teams and partners use every day: site evaluation, red flag analysis, scheduling, lessons learned, and the stakeholder and project tools that run across active TNC projects. You’ll work close to the metal: training and fine-tuning models, designing agent workflows against NOS data and tools, wiring up MCP interfaces, and standing up the evals and monitoring that keep AI features safe in production. 

You’ll partner daily with platform engineers, product engineers, and the nuclear domain experts who use what you ship. The team is small, the pace is fast, and the work is visible, what you build will be demoed to utilities, regulators, and financial partners making 20-to-30-year decisions about the future of American nuclear power. 

Responsibilities

• Ship ML and AI features inside NOS. Train, fine-tune, and deploy models and LLM-based workflows into production, with the evals, monitoring, and safety controls that make them trustworthy. 
• Build AI agents and agent workflows. Design agents that operate against NOS data and tools, including the MCP interfaces and tool-use frameworks they depend on. 
• Own model quality end-to-end. Define eval criteria, acceptance thresholds, and regression suites for AI features. Keep results reproducible and the bar high. 
• Partner across the squad. Work directly with platform engineers and product engineers so models reach production instead of staying in notebooks. 
• Contribute to the data ontology. Help shape how NOS data is structured so models and agents can use it reliably. 
• Build predictive and anomaly-detection models that support operations and engineering decisions, including time-series analysis of sensor and operational data from active projects. 

Required Experience

 

 

• Bachelor’s or Master’s in Computer Science, Machine Learning, Statistics, or a related technical field, or equivalent production experience. 
• 5+ years of professional experience shipping ML or AI features into production. We care about what you’ve shipped, not just what you’ve studied. 
• Strong Python and hands-on experience with modern ML and LLM tooling (PyTorch, Hugging Face, or similar). 
• Production experience with LLMs and agents: prompting, fine-tuning, RAG, evals, tool u