Explore the margin between Artificial Intelligence and Materials Science. Join us in creating a unique object fingerprint which is impossible to replicate, thus enabling an irrefutable link between physical goods and their digital records. This technology has enormous potential to tackle trillion-dollar problems from product counterfeiting to tracking items through the circular economy.

To help us achieve our goal of delivering our PUF technology to market, we are now seeking a Computer Vision Developer. This person will be responsible for developing experimental deep learning models for PUF pattern recognition and, in collaboration with the Chief Architect, building a robust machine learning pipeline.

This is a full-time position based in Copenhagen, Denmark. We are only considering candidates eligible to work in the European Union. Any applicant without current EU work authorisation and/or requiring sponsorship will not be considered.

If you’re interested in a challenge that crosses the boundaries between the physical and virtual, and if you’re keen on doing it in one of the coolest cities in the world - Copenhagen - then we’d love to hear from you!

Responsibilities

• Deploying deep learning models for registration and validation of PUF patterns.

• Collaborating with materials science team to ensure PUF materials and image capture methods maximise feature extraction capability.

• Managing relationships with ML consulting partners.

• Developing and improving deep learning pipelines for computer vision tasks.

• Researching and implementing new approaches to improve neural networks.

• Providing expert input for business development activities.

Requirements

• Minimum 2 years of solving deep learning & computer vision problems, preferably in a commercial setting.

• Solid understanding of convolutional neural networks development principles for computer vision tasks.

• Proficient knowledge and hands-on experience with deep learning frameworks (PyTorch, Tensorflow, Keras) and OpenCV.

• Strong mathematical background.

• Proficiency with at least Python language.

• Familiarity with version control tools such as Git, CVS, SVN, etc.

• Willingness & ability to lead both people and programs.

A sense of humour is a must. A tolerance for 1980s heavy metal would help.