Aerospace AI/ML Intern - Summer 2026

Description

Leidos is seeking a talented Aerospace AI/ML Intern for Summer 2026. In the Leidos Autonomy and Artificial Intelligence branch, you will work to solve complex engineering problems with a highly technical, multidisciplinary team consisting of aerospace engineers, computer scientists, research scientists, and statisticians. This group provides cutting-edge solutions to aerospace and defense problems such as autonomous battle management, robotic vehicle guidance, and model-based system identification.

Primary Responsibilities

Employees in this role will work to develop solutions to aerospace and defense problems by applying a variety of engineering, AI/ML, and software technical skills. In general, interns are presented with one or more, open-ended, research problems that are related to on-going projects. This allows interns to directly contribute to active development and make a significant impact on the greater project success.

Each intern will utilize and develop technical skills throughout the summer as they work through their specific problems. This may include topics such as dynamical modeling and simulation, machine learning (e.g., supervised learning, reinforcement learning), statistic-based analysis, and data visualization. The aforementioned skills and solutions are expected to be implemented in software via programming languages such as Python.

To succeed in this role, interns are required to leverage internal resources (e.g., software toolkits), collaborate with peers and experienced professionals, and learn independently. Each intern will also work closely with an experience mentor throughout the summer. By the end of the internship, they will have the opportunity to showcase their experience and work in company-wide events and a formal presentation.

Basic Qualifications

• Working towards a bachelor's or graduate degree in Aerospace Engineering, Computer Science, Mathematics, or a related field from an accredited college or university.

• Advanced proficiency in programming, specifically including Python and MATLAB.

• Fundamental understanding of mathematical concepts, including linear algebra, matrix math, statistics, trigonometry, and coordinate frames.

• Aerospace Engineering: Fundamental understanding of core aerospace concepts, such as vehicle and attitude dynamics, control systems, and flight mechanics.

• Computer Science / Math: Understanding of machine learning basics, supervised learning, reinforcement learning, and statistics.

• Ability to perform independent research to learn new skills.

• Ability to work both in collaborative team environments and independently on open-ended research problems.

• Effective technical communication skills.

• Must be a resident of North Alabama area, or willing to relocate to work on-site at the Huntsville, AL office.

• Ability to obtain and maintain a TS/SCI with CI Poly clearance.U.S. Citizenship required.

Preferred Qualifications

• Graduate student and / or research experience.

• Experience with C++.

• Experience with Linux.

• Experience with version control systems (e.g. Git).

• Active security clearance.

At Leidos, we don't want someone who "fits the mold"-we want someone who melts it down and builds something better. This is a role for the restless, the over-caffeinated, the ones who ask, "what's next?" before the dust settles on "what's now."

If you're already scheming step 20 while everyone else is still debating step 2... good. You'll fit right in.

For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

The Leidos pay range for this job level is a general guideline onlyand not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.