Vulnerability management and hands-on remediation

• Drive execution-heavy vulnerability management across internal reviews, bug bounty, pentests, SCA/runtime findings, and other research signals, ensuring findings are validated, prioritized, routed clearly, and tracked through remediation and verification within SLAs.
• Go beyond identifying issues: read the code, explain root cause, propose the safest fix, and directly implement or support remediation when needed for complex vulnerabilities.
• Perform hands-on validation and offensive security testing of applications and fixes, including exploit development, bypass testing, adversarial thinking, and focused red-team-style exercises, to confirm remediations address the underlying issue rather than only the initial symptom.
• Work across the kinds of application security issues common in modern SaaS environments, including authentication and authorization weaknesses, access control risks, OAuth and CSRF design flaws, SSRF, cryptographic and verification issues, information disclosure and data exposure risks, unsafe execution and deserialization patterns, and dependency or runtime vulnerabilities.
• Apply clear, risk-based severity decisions using exploitability, data sensitivity, customer impact, and blast radius.

Tooling, automation, and AI

• Configure and improve AppSec tooling and integrations, including SAST configuration, ignore lists, dashboards, and other controls that maintain useful coverage without excessive noise.
• Select, build, or refine security tooling, small automations, and workflow enrichments that reduce manual effort and scale AppSec operations responsibly.
• Use AI to automate, transform, and scale security and engineering-adjacent processes where it materially improves speed, consistency, or signal quality, while still validating outputs with strong engineering judgment.
• Embed AI-specific security checks into SSDLC reviews and code analysis, including input and output handling, AI-exposed APIs, prompt and response guardrails, and abuse or data-exfiltration paths.
• Partner cross-functionally on AI security requirements and controls so AI systems and AI-powered features are designed, deployed, and operated securely.

Engineering enablement and partnership

• Support and scale security enablement for engineers and security champions, including secure coding, AppSec, and AI-safety content.
• Provide actionable remediation guidance, secure patterns, and examples that help engineering teams fix issues quickly and correctly.
• Partner closely with Engineering, Product, Platform, Data, Legal, and other security teams to keep AppSec priorities aligned with business risk and product velocity.
• Produce clear documentation, metrics, and written narratives that improve AppSec visibility, observability, and decision-making.

What Good Looks Like at L6

• Owns meaningful AppSec goals over a semi-annual or annual horizon and independently identifies the right solutions to ambiguous, open-ended problems.
• Drives cross-team collaboration and operational improvements beyond isolated tickets or one-off reviews.
• Makes informed decisions by balancing technical detail, business context, customer trust, and long-term risk.
• Sets a high bar for ownership, communication, mentoring, and technical judgment, and helps raise the effectiveness of peers and partner teams.

Required Skills & Experience

• 5+ years of software engineering or application security experience, with meaningful hands-on AppSec depth in modern SaaS environments.
• Strong software development skills and the ability to read, write, and ship production code; Ruby experience is highly valuable, and Python or similar scripting ability is a plus.
• Strong Linux and cloud fundamentals, ideally with experience in GCP-backed environments.
• Deep familiarity with common AppSec issues, secure design, secure authentication and authorization patterns, vulnerability management, and developer security tooling.
• Demonstrated ability to perform deep code review, penetration testing, and exploit-oriented validation, and to either fix vulnerabilities directly or work closely with engineers to land durable remediations that hold up against bypass attempts and variant analysis.
• Experience handling findings from bug bounty, pentests, internal reviews, or automated security tooling through closure and verification.
• Experience using AI-assisted tools, automations, APIs, or structured workflows to improve engineering or security processes at scale.
• Experience securing AI-powered systems or features, including AI API exposure, prompt and response handling, data protection, misuse scenarios, and monitoring expectations.
• Strong written and verbal communication, stakeholder management, and influencing skills across technical and non-technical partners.

Preferred Qualifications

• Experience supporting or leading security reviews for AI-native products, internal agents, or AI-assisted engineering workflows.
• Experience improving secure-by-design practices and AppSec observability in a fast-moving engineering organization.
• Experience with security training, developer enablement, or security champions programs.
• Relevant security certifications are a plus.

Example Success Outcomes

• • Improve the health and flow of AppSec findings by keeping prioritization, remediation, and verification moving within defined SLAs.
  • Complete recurring application reviews or threat models for important systems and features.
  • Increase engineering adoption of secure patterns, AppSec tooling, and security training.
  • Reduce manual toil and improve AppSec signal quality through targeted automation and responsible use of AI-assisted workflows.

Originally posted on Himalayas