Description
As an AI Engineer at GitLab, you'll help build the foundation for the company's transformation into an AI-first company. Reporting to the Director, Enterprise AI, you'll be a hands-on technical leader responsible for delivering internal AI-powered solutions that drive measurable business outcomes.
Your initial focus will span Sales, Marketing, and Customer Support, where you will embed AI solutions into key systems and workflows. This role offers the opportunity to shape how GitLab team members work, improve flow across the organization, and help advance the company's mission in a remote, asynchronous, and values-driven environment.
Key responsibilities include diagnosing business problems before building solutions, owning AI initiatives end-to-end, designing, developing, and shipping AI-powered solutions quickly, improving organizational flow by building solutions that reduce bottlenecks, shorten lead times, and increase throughput, and integrating AI capabilities into existing systems and workflows using APIs, orchestration tools, and modern AI platforms.
You'll be expected to be Customer Zero, leveraging and showcasing GitLab's AI offerings wherever possible, feeding real-world usage insights back to R&D. You'll also partner closely with stakeholders across functions to understand the real constraints, ask the right questions, bridge technical and non-technical perspectives, and align on outcomes before jumping to solutions.
In terms of skills, you'll need to have a technologist at heart, with genuine investment in technology, the foundational and the cutting-edge in equal measure. You'll need to be competent and confident in coding skills, with the ability to build working solutions end-to-end, write clean and maintainable code, and debug effectively.
Strong proficiency in at least one modern scripting language (Python, JavaScript/TypeScript, or similar) and a solid understanding of REST APIs, GraphQL, and integration patterns are also required. Deep, practical experience with modern AI technologies, specifically prompt engineering, model selection and cost-performance trade-offs, agentic architecture patterns, and practical fluency across the LLM ecosystem are also necessary.
Finally, you'll need to have AI safety and risk awareness, with the ability to think critically about how the solutions you build could be exploited, misused, or produce unintended consequences. You'll need to be able to design appropriate guardrails and treat these as first-class engineering concerns.