Senior Application Security Engineer

Apollo.io is the leading go-to-market solution for revenue teams, trusted by over 500,000 companies and millions of users globally, from rapidly growing startups to some of the world's largest enterprises. Founded in 2015, the company is one of the fastest growing companies in SaaS, raising approximately $250 million to date and valued at $1.6 billion. Apollo.io provides sales and marketing teams with easy access to verified contact data for over 210 million B2B contacts and 35 million companies worldwide, along with tools to engage and convert these contacts in one unified platform. By helping revenue professionals find the most accurate contact information and automating the outreach process, Apollo.io turns prospects into customers. Apollo raised a series D in 2023 and is backed by top-tier investors, including Sequoia Capital, Bain Capital Ventures, and more, and counts the former President and COO of Hubspot, JD Sherman, among its board members.

Role Overview

The Senior Application Security Engineer is a senior individual contributor responsible for strengthening Apollo’s secure software development lifecycle and reducing application risk across product, platform, and AI-powered features.

This role blends deep code-level application security work with strong cross-functional partnership. It includes application security reviews, threat modeling, AppSec tooling, findings triage and remediation follow-through, external testing intake, and developer enablement.

This role is calibrated at the L6 senior-IC level: owning semi-annual or annual goals, solving ambiguous problems with sound judgment, improving operational processes, and driving meaningful cross-team collaboration and influence.

Key Responsibilities

Secure SDLC, design review, and threat modeling

Own and continuously improve the secure software development lifecycle for Apollo applications so security is embedded into design, implementation, and deployment.

Perform application security reviews, threat modeling, and deep code-level analysis for high-impact product, platform, and AI features before launch.

Provide practical security architecture guidance to Engineering, Product, and IT teams

Help define and maintain application-security guardrails, secure design expectations, code review standards, and risk models for new and existing systems.

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