GRC Analyst

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster.

The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software.

*Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab.

An overview of this role

As an Threat Research Engineer at GitLab, you'll research emerging security threats and turn your findings into protection for GitLab customers. You'll investigate new attack surfaces, including AI coding agents, agent plugins, and package ecosystems, and build systems that help detect threats and improve vulnerability advisories.
This is a full-stack role: you'll take an idea from the first research question to a working proof of concept, deploy it, and analyze the results yourself. Artificial intelligence (AI) is central to how we work. You'll use AI tools daily and build agentic workflows for research, analysis, and detection.

What you'll do

Research new attack surfaces and threat vectors, including AI coding agents, agent plugins, and package ecosystems.
Find and responsibly disclose vulnerabilities in open source projects and developer tools.
Analyze software supply chain incidents and events.
Build AI-powered workflows and agents that automate research, analysis, and detection.
Build and maintain the cloud-based systems behind our detection and advisory work.
Improve the quality and coverage of our vulnerability and malware advisories.
Write up your findings as blog posts and present them at conferences.

What you'll bring

Hands-on security experience in research, malware analysis, penetration testing, or security tooling, with an understanding of package ecosystems and software supply chain attacks.
Both a developer mindset and an adversarial mindset: you can build systems and think about how to break them.
Demonstrated experience using AI in real work, such as building with large language model (LLM) application programming interfaces (APIs) or agent frameworks like LangChain, LangGraph, or agent software development kits (SDKs).
Strong programming skills in Go or Python, with good software engineering practices, including testing, code review, continuous integration and continuous delivery (CI/CD), and writing maintainable code.
Experience deploying and running production services on a public cloud.
The ability to map attack vectors, explain trade-offs, and use data to move experiments forward, balancing quality, speed, and customer outcomes.
Clear writing for engineers and a general audience, and keeping up with security and AI industry trends.
Helpful, but not required: Google Cloud Platform (GCP); Common Vulnerabilities and Exposures (CVE) entries or published findings; research on AI agents, LLM applications, or Model Context Protocol (MCP) servers; blogs, talks, or open source security tools; Terraform or OpenTofu; databases or data pipelines at scale; reverse engineering.
You don't need deep knowledge of compilers or static application security testing (SAST) engines. A high-level understanding is enough.

About the team
We're the Threat Research group. We maintain the GitLab Advisory Database, which powers GitLab's dependency scanning, and publish our findings on the Security Labs blog.
Our work includes research on the Shai-Hulud npm worm and a critical remote code execution (RCE) vulnerability in Serena, a popular MCP coding agent.

How GitLab Supports Full-Time Employees

Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application.

Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process.

Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us.

GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know during the recruiting process.

Job Description

An overview of this role

Research new attack surfaces and threat vectors, including AI coding agents, agent plugins, and package ecosystems.
Find and responsibly disclose vulnerabilities in open source projects and developer tools.
Analyze software supply chain incidents and events.
Build AI-powered workflows and agents that automate research, analysis, and detection.
Build and maintain the cloud-based systems behind our detection and advisory work.
Improve the quality and coverage of our vulnerability and malware advisories.
Write up your findings as blog posts and present them at conferences.

What you'll bring

Hands-on security experience in research, malware analysis, penetration testing, or security tooling, with an understanding of package ecosystems and software supply chain attacks.
Both a developer mindset and an adversarial mindset: you can build systems and think about how to break them.
Demonstrated experience using AI in real work, such as building with large language model (LLM) application programming interfaces (APIs) or agent frameworks like LangChain, LangGraph, or agent software development kits (SDKs).
Strong programming skills in Go or Python, with good software engineering practices, including testing, code review, continuous integration and continuous delivery (CI/CD), and writing maintainable code.
Experience deploying and running production services on a public cloud.
The ability to map attack vectors, explain trade-offs, and use data to move experiments forward, balancing quality, speed, and customer outcomes.
Clear writing for engineers and a general audience, and keeping up with security and AI industry trends.
Helpful, but not required: Google Cloud Platform (GCP); Common Vulnerabilities and Exposures (CVE) entries or published findings; research on AI agents, LLM applications, or Model Context Protocol (MCP) servers; blogs, talks, or open source security tools; Terraform or OpenTofu; databases or data pipelines at scale; reverse engineering.
You don't need deep knowledge of compilers or static application security testing (SAST) engines. A high-level understanding is enough.

How GitLab Supports Full-Time Employees

Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us.

Job Description

An overview of this role

Research new attack surfaces and threat vectors, including AI coding agents, agent plugins, and package ecosystems.
Find and responsibly disclose vulnerabilities in open source projects and developer tools.
Analyze software supply chain incidents and events.
Build AI-powered workflows and agents that automate research, analysis, and detection.
Build and maintain the cloud-based systems behind our detection and advisory work.
Improve the quality and coverage of our vulnerability and malware advisories.
Write up your findings as blog posts and present them at conferences.

What you'll bring

Hands-on security experience in research, malware analysis, penetration testing, or security tooling, with an understanding of package ecosystems and software supply chain attacks.
Both a developer mindset and an adversarial mindset: you can build systems and think about how to break them.
Demonstrated experience using AI in real work, such as building with large language model (LLM) application programming interfaces (APIs) or agent frameworks like LangChain, LangGraph, or agent software development kits (SDKs).
Strong programming skills in Go or Python, with good software engineering practices, including testing, code review, continuous integration and continuous delivery (CI/CD), and writing maintainable code.
Experience deploying and running production services on a public cloud.
The ability to map attack vectors, explain trade-offs, and use data to move experiments forward, balancing quality, speed, and customer outcomes.
Clear writing for engineers and a general audience, and keeping up with security and AI industry trends.
Helpful, but not required: Google Cloud Platform (GCP); Common Vulnerabilities and Exposures (CVE) entries or published findings; research on AI agents, LLM applications, or Model Context Protocol (MCP) servers; blogs, talks, or open source security tools; Terraform or OpenTofu; databases or data pipelines at scale; reverse engineering.
You don't need deep knowledge of compilers or static application security testing (SAST) engines. A high-level understanding is enough.

How GitLab Supports Full-Time Employees

Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us.

Job Description

An overview of this role

Research new attack surfaces and threat vectors, including AI coding agents, agent plugins, and package ecosystems.
Find and responsibly disclose vulnerabilities in open source projects and developer tools.
Analyze software supply chain incidents and events.
Build AI-powered workflows and agents that automate research, analysis, and detection.
Build and maintain the cloud-based systems behind our detection and advisory work.
Improve the quality and coverage of our vulnerability and malware advisories.
Write up your findings as blog posts and present them at conferences.

What you'll bring

Hands-on security experience in research, malware analysis, penetration testing, or security tooling, with an understanding of package ecosystems and software supply chain attacks.
Both a developer mindset and an adversarial mindset: you can build systems and think about how to break them.
Demonstrated experience using AI in real work, such as building with large language model (LLM) application programming interfaces (APIs) or agent frameworks like LangChain, LangGraph, or agent software development kits (SDKs).
Strong programming skills in Go or Python, with good software engineering practices, including testing, code review, continuous integration and continuous delivery (CI/CD), and writing maintainable code.
Experience deploying and running production services on a public cloud.
The ability to map attack vectors, explain trade-offs, and use data to move experiments forward, balancing quality, speed, and customer outcomes.
Clear writing for engineers and a general audience, and keeping up with security and AI industry trends.
Helpful, but not required: Google Cloud Platform (GCP); Common Vulnerabilities and Exposures (CVE) entries or published findings; research on AI agents, LLM applications, or Model Context Protocol (MCP) servers; blogs, talks, or open source security tools; Terraform or OpenTofu; databases or data pipelines at scale; reverse engineering.
You don't need deep knowledge of compilers or static application security testing (SAST) engines. A high-level understanding is enough.

How GitLab Supports Full-Time Employees

Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us.