AI Flight Optimization Engineering Intern

About Skydweller

Skydweller is developing the world’s largest autonomous solar-powered aircraft, designed to provide persistent, long-duration flight capabilities for defense, and commercial missions. Our aircraft combines advanced aerospace engineering, autonomy, renewable energy technologies, and artificial intelligence to enable next-generation aviation capabilities.

Skydweller’s engineering and flight-test programs generate significant volumes of aircraft telemetry, environmental data, and mission information. We are applying emerging AI technologies to transform this data into actionable insights that improve aircraft performance, operational efficiency, and mission planning.

About the Role

Skydweller is seeking an intern to support the development of an Agentic AI system dedicated to flight operations and optimization for long-endurance unmanned aircraft systems (UAS).

The intern will work alongside experienced aerospace, software, and AI engineers to design and prototype an Agentic AI system capable of analyzing flight-test telemetry, weather data, and mission constraints to generate recommendations that improve endurance, energy efficiency, and aircraft performance.

The intern will participate in a structured, mentored R&D experience progressing from AI fundamentals through implementation and validation of a functional prototype supporting Skydweller’s flight optimization efforts.

Why Join Skydweller?

This internship provides a unique opportunity to work on advanced aerospace technology while helping define the future of AI-assisted engineering.

Unlike traditional AI projects focused on generic applications, this role applies emerging AI technologies to a real-world autonomous aircraft program. The intern will contribute directly to the development of tools that improve aircraft endurance, mission effectiveness, and engineering decision-making.

Successful candidates will gain experience at the intersection of artificial intelligence, aerospace engineering, and autonomous systems, while working with an experienced team developing next-generation aviation capabilities.

Student Requirements

• Interns must be currently enrolled undergraduate or graduate students at an Oklahoma college/university, or CareerTech students concurrently enrolled in college-level coursework
• Due to the nature of the work, the intern needs to be a U.S. Person (US Citizen/Green Card holder)

Qualifications

• Graduate or upper-level undergraduate student in Computer Science, Aerospace Engineering, or related field
• Proven experience with Python programming
• Prior participation in R&D projects, especially in the fields of AI-agent, Aerospace, or Meteorology, is a plus
• Familiarity with AI/ML concepts (LLMs, Deep Learning, etc.), or data processing preferred

Responsibilities

The AI Flight Optimization Engineering Intern will serve in a junior, mentored software development role within Skydweller’s Research & Development environment. Responsibilities include:

• Develop Python-based tools and AI workflows using modern software development practices and Git version control
• Assist with data preparation, system testing, and validation activities
• Document code, workflows, system architecture, and write activity reports
• Participate in agile development cycles, design reviews, and technical discussions

Learning Experience

During the internship, the candidate will gain practical experience in:

• Artificial intelligence and machine learning applications for aerospace systems
• Agentic AI architectures and AI-enabled engineering workflows
• Processing and analysis of aerospace telemetry datasets
• Software development in a production-oriented aerospace environment
• Autonomous aircraft operations and flight optimization concepts

Work Environment This is a part-time 1-year internship onsite at the Skydweller Oklahoma City (OKC) Office. The role integrates the intern directly into active aerospace R&D workflows, providing exposure to advanced autonomous systems development and collaboration with AI and engineering teams.