Job description

The Nuclear Company is the fastest growing AI tech-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— Trust, 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 United States is building nuclear power again, and The Nuclear Company is writing the software that makes it possible. NOS, the Nuclear Operating System, runs the regulatory, supply chain, cost, schedule, and field work behind real reactor construction. The work matters for national security, energy independence, and the climate. You will build the AI that people use to build power plants. As an AI/ML Engineer Intern on the Platform Integration & AI/Data squad, you take real scope on the machine learning and agent capabilities inside NOS: LLM-driven workflows, retrieval-augmented generation, and agents that operate against NOS data and tools, plus the evals and monitoring that keep AI features safe in production. You work next to full-time engineers and nuclear domain experts, ship real models and features to production, and see your work put to use by the teams building plants. This is a six-month co-op available in the Spring 2027 (January to June), aligned to the academic calendar. Base location is Washington DC, on-site five days a week, with full housing and relocation for co-ops outside the DC metro area. Responsibilities Ship ML and AI features inside NOS applications teams use every day: site evaluation, red flag analysis, scheduling, lessons learned, and the stakeholder and project tools running across active TNC projects. Build AI agents and agent workflows that operate against NOS data and tools, including the MCP interfaces and tool-use frameworks they depend on. Develop data pipelines and integrations across Palantir Foundry, AWS GovCloud, and more — and contribute to the data ontology so models and agents can use NOS data 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. Help own model quality end-to-end: eval criteria, acceptance thresholds, and regression suites that keep NOS audit-ready and results reproducible; triage issues and root-cause failures. Required Experience Currently enrolled in a BS in Computer Science, Machine Learning, Data Science, Statistics, Mathematics, or a related technical field. Available for a full six-month term and returning to school afterward. Strong Python fundamentals and hands-on exposure to modern ML and LLM tooling (PyTorch, Hugging Face, or similar) through coursework or projects. A track record of shipping working software or ML projects (course projects, personal projects, or prior internships or co-ops). We care about what you've built, not just what you've studied. This position requires access to information and technology subject to U.S. export controls (including DOE 10 CFR Part 810 and NRC requirements). U.S. Person status (U.S. citizen or lawful permanent resident) is required, and TNC does not provide visa sponsorship for these roles. Willing and able to work on-site in Washington DC, five days a week, for the full six-month term. Preferred Experience Hands-on experience with LLMs and agents: prompting, fine-tuning, retrieval-augmented generation, evals, or tool use (MCP or similar frameworks). Coursework or project experience with Palantir Foundry and AIP, AWS, or another major cloud platform — Foundry exposure is heavily weighted. Solid data fundamentals: comfortable doing exploratory data analysis and working with large, messy datasets, including time-series or sensor data. Exposure to MLOps tooling (MLflow, SageMaker, Vertex AI) or CI/CD for models. Interest in energy, national security, industrial software, or regulated industries. Familiarity with nuclear engineering concepts (not required, we teach the domain). Benefits Competitive compensation packages 401k with company match Medical, dental, vision plans Generous vacation policy, plus holidays Estimated Starting Salary Range The estimated starting rate for this role is $25.00 an hour plus a $2,000 monthly housing stipend less applicable withholdings and deductions, paid on a bi-weekly basis. The actual pay 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. EEO Statement The Nuclear Company is an equal opportunity employer committed to fostering an environment of inclusion in the workplace. We provide equal employment opportunities to all qualified applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We prohibit discrimination in all aspects of employment, including hiring, promotion, demotion, transfer, compensation, and termination. Export Control Certain positions at The Nuclear Company may involve access to information and technology subject to export controls under U.S. law. Compliance with these export controls may result in The Nuclear Company limiting its consideration of certain applicants. Recruiting Fraud Alert Your safety is our priority. We want to ensure your job search stays secure. Please note that the team at The Nuclear Company only communicates through official @thenuclearcompany.com email addresses. We will never ask for payments or sensitive financial information at any stage of our recruitment process. For your peace of mind, please verify all openings and submit your applications directly through our official careers page . The Nuclear Company does not accept unsolicited resumes from recruiters or staffing agencies. Please do not contact The Nuclear Company employees directly about candidate placements.

Requirements

Must-haves

Ship ML and AI features inside NOS applications teams use every day: site evaluation, red flag analysis, scheduling, lessons learned, and the stakeholder and project tools running across active TNC projects.
Build AI agents and agent workflows that operate against NOS data and tools, including the MCP interfaces and tool-use frameworks they depend on.
Develop data pipelines and integrations across Palantir Foundry, AWS GovCloud, and more — and contribute to the data ontology so models and agents can use NOS data 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.
Help own model quality end-to-end: eval criteria, acceptance thresholds, and regression suites that keep NOS audit-ready and results reproducible; triage issues and root-cause failures.
Currently enrolled in a BS in Computer Science, Machine Learning, Data Science, Statistics, Mathematics, or a related technical field. Available for a full six-month term and returning to school afterward.
Strong Python fundamentals and hands-on exposure to modern ML and LLM tooling (PyTorch, Hugging Face, or similar) through coursework or projects.
A track record of shipping working software or ML projects (course projects, personal projects, or prior internships or co-ops). We care about what you've built, not just what you've studied.
Willing and able to work on-site in Washington DC, five days a week, for the full six-month term.
Familiarity with nuclear engineering concepts (not required, we teach the domain).

Nice-to-haves

Hands-on experience with LLMs and agents: prompting, fine-tuning, retrieval-augmented generation, evals, or tool use (MCP or similar frameworks).
Coursework or project experience with Palantir Foundry and AIP, AWS, or another major cloud platform — Foundry exposure is heavily weighted.
Solid data fundamentals: comfortable doing exploratory data analysis and working with large, messy datasets, including time-series or sensor data.
Exposure to MLOps tooling (MLflow, SageMaker, Vertex AI) or CI/CD for models.
Interest in energy, national security, industrial software, or regulated industries.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningllmawspythonci/cd

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k