Senior Application Security Researcher

Toronto, ONOnsiteFull TimeSeniorPosted 1 month ago

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Required skills

Application Security Researcher
AI-driven SDLC
Offensive Security
Vulnerability Research
Application Security
Web Application Vulnerabilities
API Vulnerabilities
Business Logic Flaws
Attack Chains
Python

Nice to have

Published Research
CVEs
Conference Talks
Bug Bounty
AI Agents
LLM Evaluation Frameworks

Role summary

Seeking a Senior Application Security Researcher to advance AppSec within an AI-driven SDLC. This hands-on role involves building and breaking systems, focusing on next-generation detection and autonomous pen-testing. Responsibilities include developing detection logic, analyzing web/API vulnerabilities, utilizing AI/LLMs for security tasks, and working with modern tech stacks like CI/CD, containers, Kubernetes, and cloud platforms. Requires strong coding in Python/Go, data analysis skills (SQL/BigQuery), and clear technical communication. A Master's degree in Computer Science or Cyber Security is required, with 5+ years in offensive security or vulnerability research.

The company secures the AI-driven SDLC from prompt to production, unifying development and cloud context to stop vulnerabilities at the source. The Security Research group is hiring a senior, hands-on Application Security Researcher to push modern AppSec forward — working with engineers, researchers and AI/data scientists on next-generation detection, including autonomous, agentic pen-testing capabilities. This is a build-and-break role, not a typical AppSec position.

Requirements

Must-have skills:

5+ years hands-on in offensive security, vulnerability research, or application security
Deep understanding of web application and API vulnerabilities, including business-logic flaws and multi-step attack chains
Strong coding in Python, Go, or similar, with production-quality code shipped
Experience building or tuning detection logic (SAST, DAST, SCA, secrets, or custom rule engines) and reducing false positives
Solid grasp of modern stacks: CI/CD pipelines, containers, Kubernetes, and at least one major cloud provider
Hands-on use of LLMs / AI models for security tasks, with the judgment to measure where they help and where they fail
Comfort with large datasets (SQL, BigQuery, or similar) to drive research and measure detection accuracy
Takes research ideas from prototype to production with minimal guidance
Clear written communication — can explain a complex attack path to engineers and product managers
M.Sc. in Computer Science, Cyber Security, or a related field

Nice to have:

Published research, CVEs, conference talks, or a bug bounty track record
Experience building AI agents or evaluation frameworks for LLMs
Background in exploit development, red teaming, or penetration testing
Code analysis techniques (taint analysis, call graphs, reachability)
Contributions to open-source security tools

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