Offensive Security Agent Engineer
Pensar
Offensive Security Agent Engineer
Pensar
AI-driven continuous penetration testing platform
Pensar · New York, United States · — · Posted 2026-08-28
Salary: USD 175,000–225,000
Workplace: on_site
We are seeking an Offensive Security Agent Engineer to build the systems that power autonomous offensive security across Apex and the Pensar platform. You'll work at the intersection of software engineering, offensive security, and AI agents, building the harnesses, tools, execution environments, and workflows that allow autonomous agents to discover and exploit vulnerabilities in real-world applications.
This is an engineering role focused heavily on web security and agentic systems. You'll work on everything surrounding the model itself: how agents interact with browsers, terminals, proxies, scanners, and custom security tools; how they maintain context and reason across long-running engagements; how they explore complex applications; and how we evaluate whether they are actually becoming better offensive security operators.
You'll work closely with our security researchers, AI engineers, and product engineers to turn offensive security techniques into reliable autonomous capabilities. When an agent fails to find a vulnerability, gets stuck in an application, uses a tool incorrectly, or takes an inefficient attack path, you'll dig into why and build the systems that make it better.
The ideal candidate is a strong software engineer who understands how modern web applications break, is deeply interested in agentic systems, and wants to build autonomous security infrastructure rather than simply use existing AI tooling.
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We are an equal opportunity employer committed to diversity and inclusion. We welcome applications from all qualified candidates regardless of race, gender, age, religion, sexual orientation, or disability status.
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Pensar offers a continuous penetration testing platform called Apex that integrates into the software development lifecycle to test security at every deployment. It uses AI agents to identify, exploit, and remediate vulnerabilities, guided by a dynamic threat model built around a client’s business logic to uncover novel attack paths. A core feature is auto-remediation, generating a patch as a pull request for developers to review and merge once a vulnerability is confirmed. The platform supports both blackbox and whitebox testing, reduces false positives, and aims to help enterprises securely accelerate software delivery by embedding AI-assisted security into CI/CD workflows.
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