Drimva

Apollo.io

Staff Application Security Engineer

$225K–$324K

Remote, United States +1 · Remote · Staff · Full-time · Posted Oct 16, 2026

Location
Remote, United States
United States
Experience
Typically 10+ years in software engineering or application security.
Visa sponsorship
Applicants outside the US may request the annual salary range for their location during the interview process.

About the job

Leads Apollo’s application security strategy and hands-on security work across product, platform, and AI-powered systems, while establishing practices and tools adopted across engineering teams.

What you'll do

  • Set and advance AppSec strategy, standards, and cross-team initiatives; influence engineering roadmaps and mentor security engineers and champions.
  • Lead architecture reviews, threat modeling, vulnerability investigation, exploit development, and risk-based remediation for complex systems.
  • Apply AI and automation to AppSec workflows and define security approaches for AI systems and agent features.

What they ask for

  • Track record of leading AppSec strategy across multiple engineering teams with measurable results; typically backed by 10+ years in software engineering or application security.
  • Expertise in code review, penetration testing, and exploit development, plus strong software development skills and ability to ship production code.
  • Deep experience in secure architecture, authentication and authorization, and security building blocks adopted by other teams.
  • Hands-on experience securing AI-powered systems or agent features.

Nice to have

  • Ruby and Ruby on Rails experience; Python is a plus. Strong Linux and cloud fundamentals, ideally GCP and Kubernetes.
  • Experience with AI-native product security, security champions or developer enablement; public security work or OSCP/OSWE certifications.

Pay

$259,200 — $324,100 USD (total cash compensation); $225,400 — $281,800 USD (total cash compensation)

Tools

React, Ruby, Ruby on Rails, Python, MongoDB, Elasticsearch, Kubernetes, GCP

AI focus

Use AI and agents for code review, threat modeling, triage, and remediation; secure AI systems and agent features, including their APIs, prompts, tool permissions, and data-exfiltration paths.

Model-written summary · the original posting is authoritative

First seen Oct 27, 2026 · Last verified Nov 5, 2026 (UTC)