Software Engineer II - ADEX at Abnormalsecurity

AbnormalsecurityHybrid - Bangalore, India
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About this role

<h2>About the Role</h2> <p>Enterprises of all sizes trust Abnormal's AI-native security products to stop cybercrime and protect critical communications, identities, and infrastructure in the cloud. Our products are <strong>data- and systems-intensive</strong>, operating at high scale and low latency across multiple clouds and regions.</p> <p>As a <strong>Software Engineer II on the ADEX Team</strong>, you are a highly capable detection feature owner: you take a detection problem, come up with an idea, design a technical approach, and drive it end-to-end, from design and implementation through launch, operation, and continuous improvement. You will work with a world-class group of engineers, product managers, and data scientists to build and operate detection that is <strong>reliable, scalable, and AI-native by default</strong>.</p> <p>This role focuses on <strong>impersonation detection</strong>, including brand, lookalike-domain, VIP and employee impersonation. It is ideal for an engineer who has already shipped meaningful production systems, wants more ownership and impact, and is excited to use AI to build detection that was not possible before.</p> <h2>About the Team</h2> <p>The Advanced Detection of EXtended threats (ADEX) is an attack-detection team inside Abnormal's Detection org. We own several of the highest-visibility detection surfaces at the company, spanning attachment-based attacks, fraud, and impersonation. We work the way an analyst would: we study the attacks that get through, understand the underlying pattern, and translate it into system-level detection enhancements that generalize beyond the individual attack.</p> <p>We are also one of the most AI-forward teams at Abnormal. We build and operate LLM-based detection agents and treat internal AI tooling as a first-class deliverable. Every engineer here writes detection logic and builds AI agents. Impersonation is one of the most damaging and visible classes of attack we defend against, where even si

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