Back-end engineer (Video back-end)

About product:

This is an intelligent video analytics platform designed to automate the process of learning and generating contextual insights from the unstructured data inside video footage. The company’s platform uses artificial intelligence built on proprietary machine comprehension services that can contextually understand the content of user’s data, enabling clients to provide contextual advertisements and product recommendations to provide a personalized experience for users. They have developed and deployed a computer vision system for industrial procedure monitoring which involves multiple cameras, on-premise servers with multiple GPUs, and a cloud component on AWS cloud. The system is installed in 3 countries, and we have new customers all over the world.

About the role:

We are looking for a Back-end (Video back-end) vision engineer experienced in video streaming software. You will be expected to build a real-time production ready ML Inference pipeline using GStreamer. You will be responsible for optimizing and stabilizing a pipeline capable of streaming multiple video feeds with minimal latency on a local network. Furthermore, you may also be responsible for any accessory microservices and tasks needed in addition to the vision pipeline. For example, visualizations or cleaning, filtering, and preparing data to upload to the cloud.


— 3+ years of relevant experience

— Experience with Python or C/C++

​​​​​​​— Experience with G Streamer, Deep stream, or GPU programming

— Strong knowledge of algorithms, data-structures, OO design concepts

— MS or BS in Math/Computer Science/etc

— Good written and spoken English.

Nice-to-have skills:

— Development with G Object and G Lib highly desirable

— Knowledge of video encoders

— Familiarity with computer vision.

You will work closely with the founding team and will have a great impact on the direction of the products and the company. If you’re interested in joining our new R&D team, please let us know about yourself.


Location: Remote work
Type: Full-time

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