Machine Learning Intern

The data science group in the Leidos Innovation Center is looking for a Machine Learning Intern to support ongoing research in a variety of application areas. The intern(s) will be involved in developing and adapting novel machine learning technologies to address long-term problems of importance to national security. The role is for 15+ hours per week and can be remote or hybrid, depending on candidate preference and proximity to an existing Leidos office.

Primary Responsibilities

• Contribute to complex data-driven development projects involving diverse data types using modern ML architectures
• Work with teams of other data scientists, research scientists, and engineers using an Agile development approach
• Prototype, implement, and test existing state-of-the-art as well as novel ML algorithms to solve customer problems

Basic Qualifications

• Must be enrolled in Master's Degree program in Engineering, Physics, or Mathematics.
• Proficiency in Python, PyTorch or Tensorflow, Pandas, and Scikit-learn
• Experience solving a variety of machine learning problems (e.g., supervised and unsupervised learning techniques, statistical estimation, etc.)
• Ability to obtain and maintain a Top Secret clearance
• US citizenship required

Preferred Qualifications

• Direct machine learning experience involving neural network design, training, and evaluation.
• Strong theoretical and practical understanding beyond a basic level of transformer models, foundation models, generative models, or graph neutral networks

Original Posting Date:

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.

Pay Range:

The Leidos pay range for this job level is a general guideline only and 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.