Machine Learning Internship Summer 2027 New Durham, NC

CoVar, LLC; Durham, Northern (NC, KY); Hybrid; USD 28,000 - 41,000; Part time

Benefits offered by this job

Hybrid policy (in office 3x per week)

Competitive hourly wages

Relocation assistance available

Job summary

CoVar, LLC in Durham, NC is offering an AI/ML internship spanning 8 to 12 weeks. You will develop software and machine learning algorithms to address real-world customer problems, work with data, evaluate results, and write production code.

You may present your work to DoW stakeholders or industry partners. Applicants should have strong Python and PyTorch skills, familiarity with NumPy and Pandas, and an understanding of ML fundamentals.

Qualifications

• Experience with Python ML libraries and data processing.
• Knowledge of gradient descent, cross-validation, ROC curves.

Responsibilities

• Develop software and ML algorithms to solve real-world customer problems.
• Write production code that goes onto real-world systems.
• Present work to DoW customers or industry stakeholders.

Skills

Python

PyTorch

NumPy

Pandas

Git

Machine Learning fundamentals

Computer Vision

Education

Pursuing BS/MS/PhD in engineering/math/CS

Tools

PyTorch

Git

Job description

CoVar is a small, mission-driven AI/ML R&D software company based in Durham, NC and McLean, VA. We build advanced software and machine learning systems that help the DoW detect threats in high-stakes environments and enable biomedical researchers to accelerate discoveries that save lives. Our team is composed of curious, passionate engineers who care deeply about using AI to solve real-world problems that matter.

Internship Overview

• 8-12 weeks– flexible, depending on your schedule
• Competitively paidinternship
• Matched with one project based on your current expertise and interests
• Paired with an advisor or project lead who will guide you and help you set and meet your goals
• Concludes with you presenting your work either internally to CoVar or externally to the customer

Interview Timeline

• Accepting applications online from now tomid-November 2026
• Interviews starting in September 2026
  • Interview steps consist of a video screening and a code screening
• Offers out by end of November 2026

About the position

You will help CoVar develop software and machine learning algorithms to solve real-world customer problems. You will work with data, develop algorithms, evaluate results, and write the production code that goes onto real-world systems. You may have the opportunity to present your work to high-level customer s in the Do W and in the industry.

Qualifications

Applicants should have e xpertise in P ython (including NumPy , pandas , and other packages) and P y T orch . Deep understanding of machine learning fundamentals (gradient descent, cross-validation, ROC curves, confusion matrices) are necessary. Knowledge of classical machine learning (e.g., support-vector-machines, logistic regression) are valued . Applicants are ideally familiar with some computer vision algorithms (e.g., for object classification ( ResNet , ResNext ) , object detection (e.g., YOLO, CenterNet ) , or image segmentatio n ) and /or other modern image processing AI/ML techniques (vision image transformers, vision language models, etc.) . Previous experience with Do W customers is a plus.

• Software expertise: Python and associated numerical and analytics packages ( NumPy , pandas , etc.) ; git ; PyTorch .
• AI/ML expertise: Machine learning fundamentals ; D eep knowledge of state -of -the -art in any of the following : computer vision (preferred) , natural language processing, classical machine learning, Bayesian models, etc.
• Pursuing B.S., preferably M.S. or Ph.D in engineering, math, computer science, or related field
• Excellent technical communication skills
• Ability to work in Durham, NC (relocation assistance available)
• Department of War project experience
• Competitive hourly wages
• Hybrid policy (in office at least 3x per week)

Eligibility for a US security clearance is required for this role, and that requires US citizenship.

Voluntary Self-Identification

For government reporting purposes, we ask candidates to respond to the below self-identification survey.Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiringprocess or thereafter. Any information that you do provide will be recorded and maintained in aconfidential file.

As set forth in CoVar’s Equal Employment Opportunity policy,we do not discriminate on the basis of any protected group status under any applicable law.

If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection.As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measurethe effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categoriesis as follows:

A "disabled veteran" is one of the following: a veteran of the U.S. military, ground, naval or air service who is entitled to compensation (or who but for the receipt of military retired pay would be entitled to compensation) under laws administered by the Secretary of Veterans Affairs; or a person who was discharged or released from active duty because of a service-connected disability.

A "recently separated veteran" means any veteran during the three-year period beginning on the date of such veteran's discharge or release from active duty in the U.S. military, ground, naval, or air service.

An "active duty wartime or campaign badge veteran" means a veteran who served on active duty in the U.S. military, ground, naval or air service during a war, or in a campaign or expedition for which a campaign badge has been authorized under the laws administered by the Department of Defense.

An "Armed forces service medal veteran" means a veteran who, while serving on active duty in the U.S. military, ground, naval or air service, participated in a United States military operation for which an Armed Forces service medal was awarded pursuant to Executive Order 12985.