Cloud Engineering Internship

Fervo Energy

Houston (TX)

On-site

USD 25,000 - 33,000

Part time

3 days ago
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Job summary

Fervo Energy is offering a Cloud Engineer Intern role where you’ll join the IT team to build and maintain cloud infrastructure powering data, applications, and internal tools. You’ll run a 12‑week project, present results to executives, and gain a real seat at the table with a potential full-time path.

Ideal candidates are rising juniors/seniors pursuing CS/IT or related fields, with familiarity with at least one cloud platform (AWS/Azure/GCP) and a desire to learn, collaborate, and deliver

Qualifications

  • Rising Junior, Senior, Master’s/MBA candidate wrapping up within the next year, or PhD candidate — pipeline toward full-time offers
  • Pursuing a degree in Computer Science, Information Technology, 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
  • Familiarity with at least one cloud platform (AWS, Azure, or GCP)

Responsibilities

  • Support design, deployment, and maintenance of cloud infrastructure (e.g., AWS, Azure, or GCP)
  • Assist with infrastructure‑as‑code and automation efforts
  • Help monitor system performance, reliability, and security
  • Support cloud cost optimization and resource management
  • Troubleshoot and resolve infrastructure issues across environments

Skills

Communication skills
Willingness to learn
Cloud familiarity

Education

CS/IT-related degree

Tools

Terraform
CloudFormation
Docker
Kubernetes

Job description

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 development in Milford, Utah, one of the largest next‑generation geothermal developments in the world. As a Cloud Engineer Intern, you'll join Fervo's IT team to help build and maintain the cloud infrastructure that powers the company's data, applications, and internal tools. You'll work alongside our IT and engineering teams to support cloud architecture, automation, and reliability efforts as Fervo scales its infrastructure to match its growth.

Requirements
Responsibilities
  • Support design, deployment, and maintenance of cloud infrastructure (e.g., AWS, Azure, or GCP)
  • Assist with infrastructure‑as‑code and automation efforts
  • Help monitor system performance, reliability, and security
  • Support cloud cost optimization and resource management
  • Troubleshoot and resolve infrastructure issues across environments
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, Information Technology, 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
  • Familiarity with at least one cloud platform (AWS, Azure, or GCP)
Preferred Qualifications
  • Experience with infrastructure‑as‑code tools (e.g., Terraform, CloudFormation)
  • Familiarity with containerization (Docker, Kubernetes)
  • Scripting experience (Python, Bash, or similar)
  • Interest in cloud security and cost optimization
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