Founding Engineer

An elite, high-profile Cybersecurity & AI-Native startup that has recently emerged backed by a massive, top-tier funding round from world-renowned cyber venture funds.

Operating currently in Stealth Mode, the company is engineering a revolutionary, AI-native management and automated investigation platform that completely replaces fragmented, legacy enterprise ticketing systems (such as Jira) within security environments.

Operating as a highly selective, foundational core group, this is an exceptional, high-equity opportunity to join the founding engineering team as they build a market-disrupting infrastructure from scratch.

The company's corporate headquarters is located in a prime Tel Aviv location (exceptionally accessible, situated within immediate walking distance of major train and light rail lines) and operates on an intensive, high-velocity 5 days in-office model.

Position Overview-

• Founding Core Full-Stack Engineer joining an elite R&D squad of 7 developers to design, architect, and implement the platform’s foundational engine completely from scratch.
• Writing performance-critical, data-intensive backend architectures primarily in Python (~80% of the stack) deployed over robust AWS cloud infrastructures.
• Developing slick, intuitive, and highly responsive user interfaces utilizing React to visualize complex attack paths and forensic timelines.
• Working in direct collaboration with the CTO to integrate advanced LLMs and cognitive automation agents natively into the core product architecture.

Requirements-

• 5 years of proven professional software engineering experience in Python, Go, or C++ within product-centric organizations (Mandatory).
• Strong, hands-on production experience building user interfaces using React (Mandatory).
• Solid technical proficiency architecting and deploying systems over public cloud environments, specifically AWS (Mandatory).
• Deep, practical development experience operating within Linux environments (Mandatory).
• Practical understanding of building event-driven architectures, distributed systems, or integrating foundational LLM/GenAI components is highly advantageous.