AI Applications Engineering Internship

Job Type Internship Description Internship Overview You won't be running coffee orders or shuffling paperwork this summer. At Fervo, interns are handed something real: a project of your own, scoped with your manager on day one and yours to drive for the full 12 weeks. You'll work side-by-side with the teams building the next generation of geothermal energy, tackling problems that genuinely move the business forward.

At the end of the summer, you'll present your work to our executive leadership team, department leads, and fellow interns, sharing real results with a real audience. This is a real seat at the table — and a real shot at what comes next. Position Description Fervo Energy is developing next-generation geothermal power to deliver firm, carbon-free energy at scale, anchored by our flagship Cape Station project in Milford, Utah.

We're building a dedicated AI team to unlock transformational value across drilling, reservoir modeling, operations, and commercial strategy, and we're looking for a graduate-level AI Applications Engineering Intern (PhD candidates strongly preferred) to help lead the way. You'll apply cutting-edge AI to real problems in geothermal development, from hybrid AI-physics models for subsurface forecasting to RAG systems for knowledge management and predictive maintenance models, serving as an internal consultant on Fervo's Strategy Team and partnering with end-user departments to guide decision-makers through complex technical, operational, and commercial challenges

Requirements

Responsibilities Develop, train, and evaluate advanced AI models (LLMs, ML, time-series, hybrid physics-informed) Collaborate with end-user teams to scope and deliver applied AI solutions Contribute to Fervo’s centralized AI infrastructure and data architecture Document methodologies and provide clear technical communication to technical and non-technical stakeholders Present findings and recommendations to cross-functional teams, including senior leadership

Required Qualifications

Graduate student or PhD candidate in Computer Science, Applied Mathematics, or a related quantitative field with a focus on AI/ML Strong proficiency in Python and machine learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn) Demonstrated research experience in one or more of: large language models, time-series analysis, physics-informed ML, optimization, or reinforcement learning Ability to apply theoretical knowledge to practical, messy, real-world datasets Excellent problem-solving, communication and collaboration skills Self-starter with the ability to scope and drive projects independently

Preferred Qualifications

Experience with energy systems, industrial operations, or geoscience applications Prior experience with RAG architectures, data engineering, or scalable model deployment