Machine Learning Engineer – focus on Computer Vision and Classical ML
Do you want to work with real live systems where your models make an impact in production – not just in the lab? We are looking for a Machine Learning Engineer with a focus on computer vision and classical machine learning to join our team.
You will join a small, highly skilled team that currently consists of three people and is looking to grow with one or two more colleagues. Together, you will develop the next generation of machine learning solutions – from initial idea to production-ready product. You will work both hands-on and strategically to improve existing systems, making them more robust, modern, and increasingly automated.
Our systems are live or near-live, which means that fast and resource-efficient solutions (under one second response time) are crucial. You will therefore work across the stack – from model development and data handling to implementation, optimization, and product integration.
Develop and improve AI and ML systems for near real-time data in resource-constrained environments
Contribute to the entire development lifecycle – from idea and prototype to production deployment
Modernize and automate existing solutions together with the team
Write, test, and maintain efficient and well-structured code
Work closely with both developers and non-developers to build robust, user-friendly systems
We are looking for someone with an engineering background and experience in both backend development and machine learning/statistics. You enjoy seeing things work in production – not only in theory.
We believe you:
Have 3–5 years of relevant experience (more or less is also fine)
Are used to implementing ML solutions in production
Have experience with statically typed languages such as Go, Java, C, or C++
Have experience with Python and frameworks such as PyTorch and ONNX
Have a solid understanding of fundamental ML principles
Can quickly learn new tools, languages, and frameworks
It is a plus if you:
Personal qualities we value highly:
