Sr Staff Security Researcher (AI-Assisted Vulnerability Research)

Palo Alto Networks•Santa Clara, CA

9d•$139,600 - $225,775•Onsite

About The Position

This is a research-heavy role for a self-directed researcher-builder. The ideal candidate can independently identify high-impact security problems, build reliable harnesses and evaluation pipelines, analyze large-scale vulnerability data, and drive projects toward concrete outcomes such as improved harness capabilities, validated findings, technical reports, benchmarks, responsible disclosures, open-source tools, CVEs where appropriate, or production-impacting security workflows. We prioritize finding quality and research impact over raw vulnerability counts.

Requirements

Master's degree in Computer Science, Cybersecurity, or a related technical field, or equivalent practical experience.
Demonstrated ability to independently drive a technical research project from problem formulation to implementation, evaluation, and written results.
Evidence of original security research or high-signal technical output, such as CVEs, responsible disclosures, bug bounty findings, security conference papers, technical writeups, GitHub projects, fuzzers, harnesses, exploit analyses, AI/security benchmarks, open-source security tools, or comparable research artifacts.
5+ years of experience in vulnerability research, offensive security research, reverse engineering, fuzzing, exploit development, program analysis, security automation, or a closely related security research role.
Demonstrated experience in one or more of the following: vulnerability research, reverse engineering, fuzzing, exploit development, root-cause analysis, exploitability assessment, PoC development, patch analysis, program analysis, or security tooling.
Experience designing or building reproducible security experiments, including target setup, harness development, validation logic, oracle design, evaluation metrics, false-positive analysis, or reporting workflows.
Strong programming skills.
Strong knowledge of modern operating systems, network protocols, application security, software vulnerability classes, and common exploitation or validation techniques.
Strong written communication skills, including the ability to document methods, evidence, limitations, reproduction steps, impact, and remediation guidance clearly.

Nice To Haves

PhD in Computer Science, Cybersecurity, AI/ML, Systems, Programming Languages, or a related field, or equivalent demonstrated research experience.
Experience building AI agent harnesses, fuzzing harnesses, evaluation harnesses, vulnerability validation workflows, exploitability triage systems, patch validation pipelines, security benchmarks, or open-source vulnerability research tooling.
Experience handling real vulnerabilities end-to-end, including target selection, environment setup, harnessing, reproduction, root-cause analysis, exploitability assessment, patch comparison, responsible disclosure, and maintainer communication.
Knowledge of security in one or more of the following areas: Web Security, OS & Kernel Security, Browser Security, Software Supply Chain Security, OT/IoT Security, Network/Protocol Security, Cloud Security, Application Security, file parser security, or protocol parser security.
Strong practical artifacts are highly valued.
A public track record of security research, such as conference presentations, publications, CVEs, responsible disclosures, bug bounty results, technical blogs, GitHub projects, open-source security tools, AI/security benchmarks, agent frameworks, or security research artifacts.
High-impact maintainer relationships, experience reporting vulnerabilities to major open-source projects, or a track record of clear, actionable, well-received vulnerability disclosures is a strong plus.

Responsibilities

Design, build, and improve AI/security harnesses for vulnerability research, with emphasis on reproducibility, validation quality, exploitability clarity, false-positive reduction, and stable evidence generation.
Produce high-quality research and security artifacts, such as improved harness capabilities, validated findings, root-cause analyses, technical reports, benchmarks, internal research artifacts, open-source tools, responsible disclosures, publications, or CVEs where appropriate.
Conduct deep technical analysis across real-world software and open-source projects, including reverse engineering, fuzzing, root-cause analysis, exploitability assessment, patch analysis, variant analysis, and PoC validation.
Build reusable research infrastructure, including target setup automation, fuzzing harnesses, AI agent workflows, benchmark environments, validation oracles, triage pipelines, evaluation metrics, and maintainer-facing reporting workflows.
Use LLMs, AI agents, fuzzing, static/dynamic analysis, program analysis, reverse engineering automation, and security automation to improve the quality, speed, coverage, and reliability of vulnerability research workflows.
Analyze large-scale harness outputs, including successful findings, failed attempts, crash clusters, validation traces, false positives, patch comparisons, and target patterns, to identify new research opportunities and improve future harness capabilities.