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. It provides actionable insights to help businesses in a variety of industries. Join us as we continue to expand, grow, innovate, and help take Scandit to the next level.
Our newest product, ShelfView, is a machine learning-powered platform for retail that enables real time shelf visibility and empowers more intelligent and efficient store operations. With our scalable data capture technology via fixed cameras and mobile devices, retailers are on track to save tens of millions in operating expense and in turn generate tens of million more in value for their customers.
We are looking for a new colleague to join the ShelfView team and work on creating digital twins of retail shelves.. You will be responsible for evolving our current product by adding new features and optimizing the existing offering. You will work on computer vision and deep learning algorithms to improve shelf recognition beyond state of the art.
If these challenges sound interesting to you, we’d love to hear from you!
You are an experienced ML engineer who is passionate about building, shipping, and improving machine learning-powered systems. You have a proven track record of delivering state-of-the-art computer vision systems with a cloud component and firsthand experience bringing them from the prototyping stage to production.
You recognize that software development encompasses both technology and human interaction and feel comfortable working independently or on a team. While you stay on top of the state-of-the-art in CV/ML, you value proven technologies and small increments. You are creative and determined to solve real-world problems by thinking outside the box and making an impact. You thrive on tackling open problems, taking ownership, and effectively overcoming obstacles. You pay attention to details and at the same time are able to take pragmatic shortcuts to reach our goals.
In particular, we are excited to hear from you if the following sounds like you:
Here are just some of the reasons why people choose to build their career at Scandit:
Scandit enables enterprises and consumers to change the way they interact with everyday objects and augment the physical world with real-time data captured by scanning barcodes and recognizing text, objects, and other visual identifiers using smartphones, tablets, wearables, drones and robots.
Scandit’s mobile data capture platform is built on proprietary computer vision, augmented reality, and machine learning technologies. Companies in industries such as retail, transportation & logistics, manufacturing, and healthcare can use Scandit’s technology to create and power mobile apps for crucial enterprise workflows like mobile point of sale, mobile shopping, self-checkout, inventory management, and proof of delivery.
Many of the world’s most innovative and successful companies are benefiting from Scandit’s enterprise-grade mobile data capture platform, including Sephora, Nissan, DHL, and Levi Strauss & Co.
Scandit was founded by a group of researchers from ETH Zurich and MIT. The company is headquartered in Zurich and is also represented in Boston, London, Warsaw, Tokyo and Tampere. Over the years, our company has evolved into a world-class team of mobile image processing, cloud-computing and “Internet of Things” experts from around the globe and has most recently also opened an office in Tampere, Finland.
“Everybody is welcome here” - Is a celebrated component of our DNA.
At Scandit we strive to create an inclusive environment that empowers our employees. We believe that our products and services benefit from our diverse backgrounds and experiences and are proud to be a safe space for all.
All qualified applications will receive consideration for employment without regard to race, colour, nationality, religion, sexual orientation, gender, gender identity, age, physical [dis]ability or length of time spent unemployed.