2027 Internship - AI & Data Science - Analytic Capabilities

Johns Hopkins Applied Physics Laboratory•Laurel, MD

About The Position

The Johns Hopkins University Applied Physics Laboratory (APL) is seeking interns to help address complex research, engineering, and analytical challenges critical to the nation. APL designs and develops scalable software systems that use data science, visualization, large language models, and artificial intelligence in areas including social media analysis, healthcare, cybersecurity, and signal and image processing. Interns collaborate with a team developing solutions for national priorities and contribute to projects involving artificial intelligence, machine learning, data science, software DevOps, signal and image processing, and mathematics. APL offers a dynamic intellectual environment focused on impact, innovation, and personal development.

Requirements

Be pursuing a bachelor's, master's, or PhD in computer science, mathematics, engineering, or a related technical field.
Have a minimum GPA of 3.0 on a 4.0 scale.
Have experience with Python, Java, Kotlin, R, JavaScript, MATLAB, or C++/C.
Have an interest in or experience with AI/ML solutions.
Be able to ultimately obtain a Secret-level clearance. Eligibility requirements include U.S. citizenship; selected candidates will be subject to a government security clearance investigation and must meet requirements for access to classified information.

Nice To Haves

Experience working with large data sets, pattern recognition, or statistical analysis.
Practical experience developing software using Git, Maven, Poetry, or similar build tools.
Practical experience working with web interfaces, visualization tools, or frontend frameworks such as React, Angular, or Vue.
Practical experience and/or interest in LLM engineering, such as prompt design, retrieval-augmented generation (RAG), or agentic frameworks.

Responsibilities

Collaborate with a team to create solutions addressing national priorities and delivering real-world impact.
Contribute to projects involving artificial intelligence, machine learning, data science, software DevOps, signal and image processing, and mathematics.