ML Inference Engineer

The world's leading retailers trust and rely on Everseen's Visual AI™ solutions to improve their bottom line by minimizing shrink, streamlining operations via hyper automation, and delivering a better customer experience. 

Everseen’s Visual AI™ is a comprehensive process aware platform that delivers Checkout Intelligence, Shelf Intelligence, Supply Chain Intelligence, Car Lot Intelligence, Production Line Intelligence, and Generic Process Automation Applications, transforming how businesses see and solve their most costly problems. The company’s ground-breaking AI technology processes over 200 years of video footage every day and protects ~$500B worth of assets.

Everseen has earned multiple industry accolades, including 5 consecutive years as Gartner’s Top Pick for Retail Technology Innovation, Deloitte’s Tech Fast 50 winner, and Google & Deloitte’s new Tech award winner. Everseen is headquartered in Ireland, with its US Head office in Miami as well as R&D Centres in Timisoara, Romania; Belgrade, Serbia; Barcelona, Spain and India. For more information visit www.everseen.com

Position mission

We are looking for Engineers with Machine Learning and Computer Vision background who want to work on the leading edge of automation in retail. Passionate individuals with expertise in Machine Learning and Computer Vision, are encouraged to join the team as we try to create a dynamic, highly active and skilled group that is about to change the video analytics landscape. Salary budget depends on a seniority.

Main responsibilities

• Optimizing neural network graphs using machine learning techniques (e.g. pruning)
• Converting neural network graphs to fit requirements for a specific hardware (e.g. Nvidia, OpenVino, etc)
• Presenting and documenting model optimization/conversion results

Successful candidature requirements

• BS or MS in computer science, electrical engineering, mathematics/statistics, other relevant technical university, or enthusiasts with at least intermediate level of experience in the before mentioned domains
• The applicants should understand machine learning concepts from both theoretical and practical perspectives
• Programming experience in Python
• A suitable candidate should be familiar with some of computer vision, machine learning and deep learning libraries (e.g. OpenCV, TensorFlow, Caffe, Keras, PyTorch, Scikit-learn…) and software stacks (e.g. TensorRT)
• Experience with ML accelerators and hardware architecture
• The candidate must be a highly creative out-of-the-box problem solver, capable of proposing novel solutions to problems, performing experiments to show feasibility of their solutions and working to refine the solutions into a realworld context

Preference:

• Preference will be given to candidates with practical/industrial experience in the field of machine learning with special focus on computer vision