Cyber AI/ML Intern​

Description

The Leidos Cyber Accelerator is redefining how we build, validate, and scale next-generation cyber and artificial intelligence (AI) capabilities. We are seeking an AI/ML Intern to support Cyber AI projects.

The ideal candidate is passionate about a broad range of projects from research & development to prototype development, culminating in deployment of our products to customers. This role works alongside junior and senior engineers and research scientists with expertise in machine learning, cybersecurity, IT, reverse engineering, and Cyber AI. The successful candidate will have experience in areas like: software engineering, artificial intelligence (AI), cybersecurity, and machine learning (ML).

Primary Responsibilities

• Assist in the design, implementation, and evaluation of AI/ML models for various applications.

• Create AI/ML prototypes for various use cases

• Enhance and maintain analysis tools, including automation of current processes using AI/ML algorithms

• Apply existing knowledge and acquire new skills to help solve complex problems, all while gaining experience in the development and delivery of solutions to customers

• Work within a multidisciplinary team to design and develop solutions

• Effectively communicate with engineers regarding current tasks and present findings to provide actionable insights

• Currently enrolled in a master's degree or PHD program in Data Science, Computer Science, Artificial Intelligence, Mathematics, Statistics, Computer Engineering or STEM-related degree

• Hands-on Software Development Skills (Python-Preferred)

• Understanding of machine learning algorithms, tools and platforms (Familiarity with tools like NumPy, SciPy, scikit-learn, TensorFlow, Pytorch, LLM ecosystems (RAG, agentic systems) or similar.)

• Demonstrate problem solving, teamwork, and communication skills

• Ability to obtain a DoD security clearance - US Citizenship required.

• Experience developing production solutions leveraging large language models (LLMs).

• Experience with cloud platforms (AWS, Azure) for deploying machine learning models.