data Infrastructure Engineer

We are the first Cyber security company to apply Deep Learning to Cyber security is an innovative start-up that has a unique and game-changing software solution to protect against Zero Day & APT cyber-attacks. This is an incredible opportunity to get in early at a Pre-IPO Cyber security company that is poised to do huge things! We're on a mission to disrupt the Cyber security market and the time is NOW! In the past 7 years weve faced, and continue facing, the challenges of bringing Deep-Learning technologies and performance-driven software to a single solution.
Why work with us:
We are committed to creating excellent Employee Experience. We offer competitive salaries, a generous benefits package among great culture. We have some of the most forward-thinking and hardworking people in the world working for us. What are we looking for? Were looking for an additional player to join our agentless team that would bring the world the Cyber security solution it needs. Someone who is a savvy Developer, aspiring to spearhead the design and development of a cutting-edge malware detection service, all while having fun throughout the day. Someone who isnt afraid to learn on their own while expanding the vast knowledge currently existing in the company, all in the pursue to help protect the worlds data.
Office Location:
TLV Office
Region:
Israel

Responsibilities::
As a data infrastructure engineer, you'll be at the forefront of data and ML infrastructure development. On a daily basis, you will design, develop, and maintain the systems that allow for the seamless flow, quality, and reliability of our data and deep learning models. You will be part of a cross-functional team in close cooperation with Algo and Cyber security teams and answer their needs using cutting-edge technologies. Responsibilities Develop creative and robust software solutions to automate the operation of large distributed data processing systems. Help build and support internal and external frameworks like spark, pytorch, etc to interact with various cloud technologies. Designing and implementing complex ETL pipelines. Collaborating with data scientists to develop new ML Ops processes. Keep close and overall monitoring for all the deployments of the systems, maintain the systems stability, improve the performance, the performance bottlenecks, track and troubleshoot, cost optimization.