Computer Vision/Machine Learning Summer Job 2026
About this role
Scandit gives people superpowers. Whether enabling delivery drivers to make quicker deliveries, matching a patient with their medication, or allowing retailers to make store operations more efficient, our technology automates workflows and provides actionable insights to help businesses in a variety of industries. Join us, as we continue to expand, grow and innovate, and help take Scandit to the next level.
About the Summer Job
We are looking for students with a passion for computer vision or machine learning to join one of our engineering teams in Tampere for three to six months. You will be a part of product development and you will help us improve and extend algorithms and their automated testing infrastructure.
Scandit is a great place to apply the theory from your studies and learn best practices used in the industry in an international team.
Responsibilities
- We have many interesting projects to offer, both in computer vision and machine learning: Research on image to sequence models for barcode scanning or state of the art image enhancement methods
- Improve barcode scanning algorithms for surfaces with deformations, glare or reflections
- Enhance real-time visual tracking and pose estimation of our products through the integration of additional sensor measurements and cutting-edge machine learning algorithms
- Enhance our real-time visualization system to analyze and develop our computer vision algorithms
- Reduce manual steps in our image annotation tools with smart algorithms
- Improve extraction of semantic information from complex documents through visual reasoning
- Our Tech StackComputer vision algorithms: C++17, CMake
- Machine learning training and tooling: Python, Pytorch
- Acceleration: SIMD, Vulkan, CoreML
- Who You AreHighly motivated student, interested in a summer job of three months or longer
- Enrolled in or completed a bachelor's or master's program in computer science, information technology or a related field
- Solid knowledge of data structures and experienc