Universal Avionics Job Category:
Software Engineer Requisition Number:
AIMLE001498
Jun 8, 2026 Full Time Job Details Description AI/ML Engineering Intern Tucson, AZ (Onsite) | Aerospace & Avionics 9/80 Schedule Available
- Every Other Friday Off AI/ML Engineering Intern
- Build AI That Powers the Future of Flight At Universal Avionics , we've spent decades building the avionics systems trusted by pilots and operators around the world. Our AI Labs team is pushing the boundaries of what's possible in aerospace technology, and we're looking for an AI/ML Engineering Intern who's ready to build real things
- not just observe.
This is not a shadow-and-sit-in role. You'll be an active contributor to the design, development, and testing of scalable AI and machine learning solutions, working alongside engineers who care deeply about getting it right in a safety-critical environment. You'll Thrive Here When... You want to build AI systems that actually ship, not just run in notebooks You enjoy translating data into models and models into measurable outcomes You're comfortable working through ambiguity and iterating fast You're curious about how AI fits into regulated, high-stakes industries You thrive in a collaborative, cross-functional engineering environment What You'll Work On Design and develop scalable AI solutions using machine learning models and tools Build and optimize data pipelines, prototypes, and training datasets using cloud platforms (AWS, Azure, or GCP) Conduct research and testing to develop machine learning algorithms and predictive models Execute performance testing to evaluate AI systems handling large datasets and complex algorithms Ensure the reliability and performance of AI systems through active development and iteration Collaborate with cross-functional teams to support project goals and solution implementation What We're Looking For Currently pursuing or holding a Bachelor's degree in Computer Science, Data Science, Engineering, or a related field Exposure to or working experience in AI, machine learning, or software development Proficiency in Python Familiarity with machine learning frameworks such as TensorFlow, Spark, or scikit-learn Experience with big data tools and cloud computing platforms (AWS, Azure, or GCP) Strong analytical and problem-solving skills Why Engineers Love Working Here Work on technology that supports real aircraft and global flight operations Gain hands-on exposure to AI development within a safety-critical aerospace environment Mentorship from experienced engineers in a specialized, mission-driven team Opportunity to grow your skills in a company with decades of aerospace credibility 9/80 schedule
- every other Friday off Our Quality Mindset & Core Values Innovation We advance avionics technology through creative engineering and continuous improvement.
Excellence We hold ourselves to high standards in quality, reliability, and execution. Resilience We solve complex technical challenges in demanding aerospace environments. "Yes, I Can" Ownership We value engineers who are proactive, collaborative, adaptable, and solutions oriented. Work Environment This role is based on-site in Tucson, Arizona. You'll work primarily in an office environment within our AI Labs team, with exposure to the broader engineering organization. ESD precautions and protective eyewear may be required in certain areas. If you're passionate about AI, machine learning, and building the systems that keep aviation safe, we'd love to connect.
Learn more:
Universal Avionics Universal Avionics' products and technology are subject to U.S. export laws and regulations, including but not limited to ITAR and EAR. As part of our compliance process, we inquire about work authorization and citizenship status to ensure alignment with these regulations. Universal Avionics Systems Corporation is dedicated to providing and promoting equal employment opportunities without regard to race, color, religion, gender, ancestry, sexual orientation, gender identity and expression, age, disability, veteran status or any other protected factor in accordance with applicable federal, state, and local laws.
