AI Applications Engineering Internship

Internship Overview

You wont 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. Youll 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 youll 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. Were building a dedicated AI team to unlock transformational value across drilling reservoir modeling operations and commercial strategy and were looking for a graduate-level AI Applications Engineering Intern (PhD candidates strongly preferred) to help lead the way.

Youll 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 Fervos Strategy Team and partnering with end-user departments to guide decision-makers through complex technical operational and commercial challenges

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 Fervos 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