Security Researcher

We are seeking an experienced Product & AI Security Researcher to investigate complex, emerging security risks across modern codebases, AI coding architectures, and LLM-backed software development workflows. In this high-impact research role, you will look beyond standard static scanners to uncover nuanced architectural vulnerabilities, permission flaws, and data-flow risks introduced by AI agents.

Working at the intersection of Application Security (AppSec), Threat Modeling, and AI-assisted engineering, you will translate deep technical research into core detection logic, threat corpora, and product features that directly protect enterprise software ecosystems.

Core Responsibilities

• Deep Security Research: Conduct technical investigations into software architectures, permission structures, API integrations, and agentic workflows to pinpoint non-obvious product security risks.
• AI & LLM Threat Analysis: Analyze how AI coding assistants, autonomous agents, and AI-first engineering practices alter vulnerability introduction, risk prioritization, and remediation patterns.
• Threat Corpus Management: Curate and evolve our core threat-pattern database, security context modules, and research-backed detection algorithms.
• Research Artifacts & Thought Leadership: Produce high-impact outputs, including technical whitepapers, evaluation benchmarks, product recommendations, and industry briefs.
• Product & R&D Integration: Partner directly with Product and Engineering teams to embed research findings into live detection mechanisms, evaluation suites, and go-to-market strategies.

What You’ll Bring

• Deep AppSec Mastery: Strong application or product security background, with an exceptional ability to evaluate code execution, system architecture, and business logic holistically.
• Research Ownership: Proven track record of executing end-to-end research initiatives—from initial hypothesis and deep technical investigation to published artifacts and actionable product impact.
• Structured Threat Modeling: Applied experience using established frameworks (OWASP, STRIDE, MITRE ATT&CK) to systematically map and remediate enterprise AppSec risks.
• Hands-on AI Engineering Context: Heavy practical exposure to AI coding agents, LLM-based development pipelines, and AI-assisted engineering tools within real-world environments.
• Communication & Autonomy: Outstanding technical writing and communication skills, with an independent mindset capable of converting research into concrete product and engineering outcomes.