IAE Machine Learning Engineer Internship - Fall 2026

University of South Florida•Tampa, FL

1d•Hybrid

About The Position

The University of South Florida (USF) Institute of Applied Engineering (IAE) provides agile, best-value engineering solutions to enhance the performance, effectiveness of its sponsors, including the Department of Defense; other federal, state, and local agencies; and industry. These engineering solutions include hardware-and-software-based research, advanced technology development, prototypes, and technical services. The position title for this job application is “Machine Learning Engineer Intern”. This internship program will complement CDAO, J8, and its component persistent workforce, enabling them to develop solutions to data-related problems. The Intern will work with the CDAO and J8 Team to develop business analytic methodologies and tools to support Special Operations resourcing decisions.

Requirements

Must be a U.S. citizen.
Current GPA of 3.0 or above.
Pursuing a bachelor's degree at a local accredited University with a Junior or Senior standing.
Ability to quickly learn and apply new technologies and tools.
Strong analytical and problem-solving skills.
Attention to detail and ability to work independently or as part of a team.
Self-motivated, detail-oriented, and eager to learn.
Good communication skills for effective collaboration with team members and stakeholders.
Strong organizational skills and the ability to manage multiple tasks simultaneously.
Experience working in groups.
Must be willing and able to work in a dynamic, rapidly changing environment
Software Design / Software app experience
AI/ML Development
Interest in Artificial Intelligence and personal projects

Nice To Haves

Security Clearance is not required but preferred.

Responsibilities

Build and maintain data pipelines within Palantir’s Maven Smart Systems (MSS) to ensure mission datasets are properly ingested, validated, structured, and optimized for analytic and machine‑learning workflows.
Design and implement AI/ML workflows inside MSS, including feature engineering, model evaluation, and preparing model outputs for operational use across CDAO and J8.
Develop MSS‑based analytic tools that support SOF resourcing analysis, enabling data‑driven tradeoffs, trend identification, and visualization of operational insights.
Create user‑facing applications and interfaces in MSS that allow analysts and decision‑makers to interact with models, dashboards, and mission datasets without technical barriers.
Prototype, test, and document machine‑learning models that address SOCOM problem sets, collaborating with engineers to transition prototypes into stable, platform‑ready components.
Integrate ML and analytic components into MSS ontologies and operational workflows, ensuring alignment with SOCOM data structures, access controls, and deployment practices.
Work directly with mission analysts to refine requirements, translating operational questions into technical tasks and ensuring outputs meet decision‑maker needs.
Support data governance and accountability through documentation, version control, and adherence to SOCOM data‑handling standards.
Participate in sprint reviews, design sessions, and technical exchanges to support rapid development cycles and alignment across CDAO, J8, and platform engineering teams.