Summer 2027 AI/ML Engineering Intern

About the Role

The Nuclear Company is a rapidly growing AI tech startup in the nuclear and energy sector. We are building the next generation of nuclear reactors using a fleet-scale, design-once, build-many approach to accelerate the deployment of safe, reliable, and affordable nuclear energy. We operate with an AI-first mindset, expecting all employees to leverage AI, technology, and the Nuclear Operating System (NOS) to improve their work.

As an AI/ML Engineer Intern on the Platform Integration & AI/Data squad, you will gain real scope in machine learning and agent capabilities within NOS. This includes working with LLM-driven workflows, retrieval-augmented generation, agents that interact with NOS data and tools, and the evaluation and monitoring systems for AI features in production. You will collaborate with full-time engineers and nuclear domain experts, ship production-ready models and features, and see your contributions used by teams building power plants.

This is a 12-week internship from May to August 2027, aligned with the academic calendar. The role is based in Washington DC, requiring on-site work five days a week. Full housing and relocation are provided for interns outside the DC metro area.

Key Responsibilities

• Ship ML and AI features within NOS applications used daily for site evaluation, red flag analysis, scheduling, lessons learned, and stakeholder/project tools across active TNC projects.
• Build AI agents and agent workflows that operate against NOS data and tools, including MCP interfaces and their dependent tool-use frameworks.
• Develop data pipelines and integrations across Palantir Foundry, AWS GovCloud, and other platforms, contributing to the data ontology for reliable model and agent use of NOS data.
• Build predictive and anomaly-detection models to support operations and engineering decisions, including time-series analysis of sensor and operational data from active projects.
• Contribute to end-to-end model quality ownership, including eval criteria, acceptance thresholds, and regression suites to ensure NOS is audit-ready and results are reproducible. Triage issues and root-cause failures.

Requirements & Tech Stack

• 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 internship term and returning to school afterward.
• Strong Python fundamentals.
• Hands-on exposure to modern ML and LLM tooling such as PyTorch, Hugging Face, or similar, demonstrated through coursework or projects.
• A track record of shipping working software or ML projects (course projects, personal projects, or prior internships/co-ops).
• Willing and able to work on-site in Washington DC, five days a week, for the full six-month term.
• Due to U.S. export controls (DOE 10 CFR Part 810 and NRC requirements), U.S. Person status (U.S. citizen or lawful permanent resident) is required. Visa sponsorship is not provided.

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 weighted heavily.
• Solid data fundamentals, including comfort with 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; domain knowledge will be taught).

Compensation & Benefits

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

Estimated Starting Salary

• $25.00 per hour plus a $2,000 monthly housing stipend, less applicable withholdings and deductions, paid bi-weekly.
• Actual pay may vary based on experience, qualifications, tenure, skill set, availability of qualified candidates, geographic location, certifications, and other pertinent criteria.