Machine Learning Internship Summer 2027
An 8-12 week paid, in-person summer internship at CoVar in Durham, NC, where you develop machine learning algorithms and production software for real-world customer problems in defense and biomedical research, working with data using Python, NumPy, pandas, and PyTorch, and presenting your work to CoVar or the customer.
- Compensation
- Not disclosed
- Location
- Durham, NC
- Work setup
- Not specified
- Seniority
- Intern
- Employment
- Internship
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Expertise in Python and associated numerical and analytics packages (NumPy, pandas, etc.)
- Proficiency with git
- Expertise in PyTorch
- Deep understanding of machine learning fundamentals (gradient descent, cross-validation, ROC curves, confusion matrices)
- Deep knowledge of state-of-the-art in at least one of: computer vision (preferred), natural language processing, classical machine learning, Bayesian models
- 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)
- Work authorization: US citizen
Nice to have
- Familiarity with computer vision algorithms (e.g., ResNet, ResNext for classification, YOLO/CenterNet for detection, image segmentation)
- Familiarity with modern image processing AI/ML techniques (vision image transformers, vision language models)
- Knowledge of classical machine learning (e.g., support-vector-machines, logistic regression)
- Previous experience with DoW / Department of War customers
Full description
About CoVar
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
- In-person in Durham, NC
- Competitively paid internship
- 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 to mid-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 customers in the DoW and in the industry.
Qualifications
Applicants should have expertise in Python (including NumPy, pandas, and other packages) and PyTorch. 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 segmentation) and/or other modern image processing AI/ML techniques (vision image transformers, vision language models, etc.). Previous experience with DoW customers is a plus.
Minimum qualifications
- Software expertise: Python and associated numerical and analytics packages (NumPy, pandas, etc.); git; PyTorch.
- AI/ML expertise: Machine learning fundamentals; Deep 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