Senior AI Engineer

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. An overview of this role As a Senior AI Engineer at GitLab, you’ll help build the foundation for GitLab’s transformation into an AI-first company, working at the intersection of applied AI engineering, business systems, and organizational design. Our team exists to help every function at GitLab realize AI’s potential to improve how work flows, how decisions are made, and how value reaches our customers. Building fast matters, but it’s not enough on its own. This role starts with understanding the real problem before writing a single line of code. You’ll develop flow and systems-thinking skills to diagnose where work actually gets stuck, distinguish symptoms from root causes, and design AI solutions that address constraints rather than adding complexity on top of broken processes. Your initial focus will span Sales, Marketing, and Customer Support, where you will embed AI solutions into enterprise systems to create leverage across the business. You’ll work across a wide range of problems: internal automation pipelines, agentic workflows, LLM-powered tooling, and productivity solutions that use GitLab’s own AI capabilities to demonstrate what’s possible. What you’ll do Diagnose business problems before building solutions. Map workflows, identify constraints, and confirm root causes before prototyping Own AI initiatives end-to-end, from stakeholder discovery and technical design through implementation, deployment, adoption, and measurement Design, develop, and ship AI-powered solutions quickly, delivering working prototypes in days, not months Improve organizational flow by building solutions that reduce bottlenecks, shorten lead times, and increase throughput for the teams you support Integrate AI capabilities into existing systems and workflows using APIs, orchestration tools, and modern AI frameworks Be Customer Zero: leverage and showcase GitLab’s AI offerings wherever possible, feeding real-world learnings back to Product and Engineering Partner closely with stakeholders across functions to understand the real constraints. Ask the right questions, challenge assumptions, and align on outcomes before building solutions Define and track success through business metrics, flow metrics, and feedback loops that make performance visible and drive continuous improvement Contribute to technical direction by evaluating tools, documenting patterns, and creating reusable frameworks that help the wider team ship faster What you’ll bring A Technologist at Heart: genuinely invested in technology, the foundational and the cutting-edge in equal measure Competent, Confident Coding Skills: you can build working solutions end-to-end, write clean and maintainable code, and debug effectively AI & LLM Technical Depth: strong proficiency in at least one modern scripting language (Python, JavaScript/TypeScript, or similar), with practical fluency across the LLM ecosystem Model selection and cost-performance trade-offs: understanding when a smaller fine-tuned model outperforms a general-purpose large one, when RAG is the right architecture, and when it isn’t Agentic architecture patterns: tool use, multi-agent orchestration, human-in-the-loop designs, guardrails, evaluation frameworks, and observability AI Safety & Risk Awareness: you think critically about how the solutions you build could be exploited, misused, or produce unintended consequences Systems Thinking & Diagnostic Rigour: the ability to look at a complex process and see the constraint Business System Expertise: familiarity with the landscape of enterprise business systems, CRM (Salesforce), marketing automation, and support tooling End-to-End Ownership: track record of owning complex initiatives from discovery through delivery Product Mindset: ability to scope MVPs, prioritise ruthlessly, and deliver iteratively Preferred: experience with GitLab platform and CI/CD workflows; background in consulting, solutions engineering, or customer-facing technical roles; experience with low-code/no-code orchestration tools (n8n, Make, Workato) alongside custom development; previous startup or high-growth company experience. About the team You will join the Enterprise Technology & AI team. We’re the backbone of the organisation, driving transformation and setting the standard for how GitLab uses technology and AI. The base salary range for this role’s listed level is currently for residents of the United States only. United States Salary Range: $108,400 – $129,600 USD.