Senior Software Engineer, Security Factory: Code Security

GitLab

8h•$139,200 - $235,200•Remote

About The Position

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. As a Senior Backend Engineer on GitLab's Security Factory: Code Security team, you help developers find and fix security issues. Those issues live in the code they write and in the open source components they depend on. Your work spans complementary parts of a complete security analysis. On the dependency analysis side, you teach the analysis engine what a project depends on. You parse dependency manifests, lockfiles, and SBOMs so the engine knows which libraries and frameworks are in play and how the code uses them. You also extend the service that turns security advisories into automated merge requests that update vulnerable dependencies. On the vulnerability detection side, you grow GitLab's security analysis engine with hybrid analyzers that blend rule-based detection and AI reasoning. You build and apply the tooling that develops, evaluates, and ships those analyzers, and you measure how well they find real problems. That work also expands coverage of the libraries, frameworks, and AI agent tooling that real-world applications are built on. These threads reinforce each other. What a project depends on, and how its code uses those dependencies, are parts of one picture, and this team works across all of it. We make thoughtful use of AI-assisted development tools in our daily work. We expect you to use them effectively and with good judgment.

Requirements

Experience building your own LLM tooling, such as a harness, an agent pipeline, or evaluations, with the judgment to know when output is trustworthy and when it isn't
Substantial professional experience writing and testing production code in a systems language, particularly Go and/or Rust, with depth in at least one. We use both, plus Ruby on the remediation side and Python for some analyzers, and value willingness to work across them.
Familiarity with package managers and dependency management in one or more language ecosystems, such as npm, Maven, pip, Bundler, or Cargo. Experience building tooling around them is better still.
Demonstrated application security experience, such as vulnerability research, secure code review, or writing detection rules, and fluency with vulnerability classes (OWASP Top 10, CWE) and the software supply chain
A track record of taking ownership of ambiguous problems and shipping with minimal guidance, with the self-motivation and organizational skills suited to a remote, largely asynchronous environment
Demonstrated capacity to communicate clearly and concisely about technical problems, and to write design proposals that bring a team to a decision

Nice To Haves

Experience evaluating AI-driven detection against labeled data, including measuring false positives and missed findings
Experience with performance optimization at scale
Hands-on program analysis experience, such as parsing, ASTs, or data-flow analysis
Familiarity with popular web or mobile application frameworks and how they handle input, data, and configuration
Experience with containerized workflows and CI/CD (we use Docker heavily)

Responsibilities

Act as the directly responsible individual (DRI) for team initiatives from design through delivery, shipping with minimal guidance in partnership with the technical lead
Bring systems built by one engineer to team ownership through documentation, tests, and shared review
Design and ship analyzers that pair deterministic analysis capabilities with AI-driven analysis, and build the evaluation harnesses that measure their false positives and missed findings against benchmark applications with known vulnerabilities, raising the bar for what counts as a trustworthy result. That includes detection rules mapped to CWE and the test fixtures that prove they work.
Package those analyzers for every place GitLab runs them, including CI jobs, AI agent workflows, and command-line tools, with findings reported in GitLab's standard security report formats
Design and ship the manifest, lockfile, and SBOM parsing that tells the static analysis engine what a project depends on, and extend it to new ecosystems and formats
Ship features across the automated remediation service that turns security advisories into dependency update merge requests, from sandboxed updates to merge request creation
Solve technical problems of high scope and complexity, actively seek out difficult impediments affecting the whole team, and advocate for improvements to quality, security, and performance with Product Management, Engineering stakeholders such as Frontend and UX, and partner teams such as Code Scanning and Composition Analysis
Mentor Intermediate engineers through code review and pairing, and maintain our internal standards for style, maintainability, and best practices through review
Participate in on-call rotations to assist troubleshooting product operations, security operations, and urgent engineering issues