Computer Vision & AI Engineer Intern (M/F)
About Vnexia
Vnexia is a Paris-based French startup specializing in Artificial Intelligence, Computer Vision, and Physical AI. We develop intelligent monitoring and safety solutions for construction sites and industrial environments using advanced computer vision, LiDAR sensors, and Edge AI technologies.
We are looking for a motivated Computer Vision & AI Engineer Intern to join our engineering team in Paris for a six-month internship starting in January or February 2027.
Role Description
As a Computer Vision & AI Engineer Intern, you will contribute to the research, development, and deployment of real-time AI solutions for industrial safety applications.
You will work on challenging real-world problems involving object detection, multi-object tracking, people counting, sensor fusion, and embedded AI deployment.
You will collaborate directly with our engineering team throughout the development lifecycle, from data preparation and model training to optimization, testing, and deployment.
Key Responsibilities
- Develop and optimize computer vision models using Python, PyTorch, YOLO, and OpenCV.
- Design and implement object detection and multi-object tracking algorithms.
- Develop people-counting and object movement analysis systems.
- Improve model performance under challenging conditions, including occlusion, lighting variations, and complex environments.
- Explore camera–LiDAR sensor fusion for real-time industrial monitoring.
- Train, fine-tune, and evaluate deep learning models using real-world datasets.
- Build efficient video processing and inference pipelines.
- Optimize AI models for deployment on edge devices such as NVIDIA Jetson.
- Participate in camera calibration, dataset annotation, and performance benchmarking.
- Research and implement state-of-the-art computer vision techniques.
Qualifications
- Currently pursuing a Master’s degree (M1/M2) or engineering degree in Computer Science, Artificial Intelligence, Robotics, Computer Vision, or a related field.
- Strong programming skills in Python.
- Good understanding of Computer Vision, Machine Learning, and Deep Learning.
- Practical or academic experience with PyTorch, OpenCV, and YOLO.
- Knowledge of object detection, image processing, and tracking algorithms.
- Familiarity with Linux, Git, and software development practices.
- Strong analytical and problem-solving abilities. • Ability to work independently and collaborate with a technical team.
- Good communication skills in English.
Preferred Qualifications
- Experience with multi-object tracking algorithms such as ByteTrack, DeepSORT, OC-SORT, or BoT-SORT.
- Knowledge of LiDAR, ROS/ROS2, and sensor fusion.
- Experience with NVIDIA Jetson, TensorRT, or ONNX.
- Understanding of camera calibration and 3D computer vision.
- Familiarity with real-time AI inference and model optimization.
- Previous academic, research, or personal projects in Computer Vision or Robotics.
Why Join Vnexia?
- Work on real-world AI systems deployed in industrial and construction environments.
- Gain hands-on experience with Computer Vision, LiDAR, and Edge AI.
- Collaborate directly with experienced AI engineers.
- Contribute to meaningful technical challenges with practical applications.
- Gain experience across the full AI development lifecycle.
- Work in a dynamic startup environment with opportunities to learn, experiment, and take ownership.
Internship Details
Company: Vnexia Location: Paris, France
Position: Computer Vision & AI Engineer Intern
Contract: Internship (Stage conventionné)
Duration: 6 months
Start Date: January / February 2027
Work Arrangement: On-site, Paris
Language: English required; French is mandatory
Compensation: Internship allowance in accordance with French regulations
How to Apply
Interested candidates are invited to apply through our website with their CV.
A GitHub profile, portfolio, or examples of previous Computer Vision projects would be highly appreciated.
Join Vnexia and help develop the next generation of intelligent AI-powered safety solutions.
Apply for this position
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