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Pensar

AI-driven continuous penetration testing platform

Offensive Security Agent Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeNew York, NY, USA$175k - $225kIn-person

About the Job

Requirements

  • At least 4 years of professional software engineering, security engineering, offensive security engineering, or equivalent experience.
  • Strong software engineering fundamentals and experience building production systems.
  • Strong programming skills in Python, Go, JavaScript/TypeScript, Rust, C/C++, or similar languages.
  • Deep understanding of modern web applications, HTTP, browsers, APIs, authentication, sessions, and application architecture.
  • Working knowledge of common web vulnerability classes and offensive security techniques.
  • Experience building or working with agentic systems, large language model applications, autonomous agents, or complex tool-using artificial intelligence systems.
  • Experience with browser automation technologies such as Playwright, Puppeteer, Chrome DevTools Protocol, or similar tooling.
  • Ability to debug complex systems across application code, infrastructure, networking, and agent behavior.
  • Comfort with Linux, containers, cloud infrastructure, and isolated execution environments.
  • Ability to independently investigate ambiguous technical problems and turn findings into production systems.
  • Interest in making autonomous systems reliable in real-world environments.
  • Bachelor's degree in Computer Science, Cybersecurity, or a related field, or equivalent experience.

Responsibilities

  • Design and build the agent harness powering autonomous offensive security workflows across Apex and the Pensar platform.
  • Build systems that allow agents to reliably interact with browsers, terminals, HTTP proxies, scanners, APIs, filesystems, and other security tooling.
  • Develop orchestration for long-running, multi-step offensive security tasks involving reconnaissance, exploitation, validation, and reporting.
  • Build abstractions that allow agents to safely and effectively execute tools, inspect results, maintain state, and adapt their approach.
  • Improve agent context management, memory, task decomposition, planning, and execution across complex engagements.
  • Design reliable execution environments for autonomous security agents operating against customer applications and infrastructure.
  • Debug agent trajectories to understand why an agent succeeded, failed, hallucinated, became stuck, or missed an attack path.
  • Build observability and debugging infrastructure for understanding agent behavior at scale.
  • Build autonomous capabilities for discovering and exploiting vulnerabilities in modern web applications and APIs.
  • Develop tooling and workflows around authentication, authorization, session management, API discovery, application state, and complex multi-step attack paths.
  • Enable agents to navigate and understand JavaScript-heavy applications, authenticated applications, APIs, and modern application architectures.
  • Integrate offensive security tooling into autonomous workflows, including proxies, scanners, browsers, custom scripts, and exploitation frameworks.
  • Implement techniques for identifying access control issues, injection vulnerabilities, server-side request forgery, authentication flaws, business logic vulnerabilities, and other application security weaknesses.
  • Translate manual offensive security techniques into primitives and workflows that autonomous agents can execute reliably.
  • Track emerging web exploitation techniques and determine how they can be incorporated into Apex.
  • Build evaluation systems for measuring offensive security agent performance against realistic targets.
  • Develop benchmarks, test environments, and regression suites that measure whether changes improve agent capabilities.
  • Analyze agent trajectories and failure modes across real engagements.
  • Identify systemic weaknesses in agent reasoning, tooling, navigation, and exploitation behavior.
  • Design experiments around prompts, models, tools, context strategies, and orchestration approaches.
  • Work with security researchers to turn newly discovered techniques into reproducible evaluations and agent capabilities.
  • Help establish metrics for autonomous pentesting performance beyond simple vulnerability detection.
  • Contribute directly to Apex, the open source autonomous offensive security tool.
  • Build reusable offensive security primitives that can be shared across Apex and the Pensar platform.
  • Design APIs and internal interfaces for agent execution, tools, environments, and security workflows.
  • Work across the stack when necessary to ship new autonomous capabilities into production.
  • Improve the reliability, performance, and scalability of systems running autonomous security engagements.
  • Collaborate with platform engineers on infrastructure for securely executing large numbers of concurrent agent workloads.
  • Help define the technical architecture of Pensar's autonomous offensive security systems as the platform scales.
  • Work closely with offensive security researchers to understand how experienced human operators approach difficult targets.
  • Convert researcher techniques, workflows, and intuition into software and agent capabilities.
  • Build experimental tooling to test new approaches to autonomous exploitation.
  • Investigate difficult targets where existing agents fail and determine what capabilities are missing.
  • Explore new approaches to browser automation, application understanding, vulnerability discovery, and autonomous exploitation.
  • Contribute to technical research and open source releases around autonomous offensive security.

About the Company

Pensar

AI-driven continuous penetration testing platform

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.

Company size
1-10
Company stage
Not listed
Total funding
Not listed
Headquarters
San Francisco, California
Founded
2015

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