Data Science & AI Engineering Internship

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 development in Milford, Utah. As a Data & AI Engineer Intern, you'll join Fervo's Data Science team to help build the data pipelines and AI infrastructure that power the company's analytics and machine learning efforts. 

You'll work alongside data scientists and engineers to design, build, and maintain data pipelines, and support the infrastructure that gets AI models into production.

Responsibilities 

• Build and maintain data pipelines and ETL processes 
• Support deployment and monitoring of machine learning models in production 
• Help design and maintain data infrastructure and architecture 
• Collaborate with data scientists to productionize models and analyses 

Required Qualifications 

• Rising Junior, Senior, Master's/MBA candidate wrapping up within the next year, or PhD candidate — we're building a pipeline toward full-time offers 
• Pursuing a degree in Computer Science, Data Engineering, or a related technical field 
• Strong written and verbal communication skills, including comfort presenting to stakeholders and leadership 
• Eagerness to learn, take initiative, and adapt quickly to new challenges 
• Proficiency in Python and SQL 

Preferred Qualifications 

• Experience with cloud data platforms (e.g., AWS, Azure, Snowflake, Databricks) 
• Familiarity with MLOps tools and practices 
• Experience with big data tools (e.g., Spark, Airflow)