AI Engineer

GitLab; United States; On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Remote work flexibility

Diversity and inclusion initiatives

Learning and development opportunities

Job summary

GitLab is seeking an experienced AI Engineer to lead the development of internal AI solutions that enhance operations across Sales, Marketing, and Customer Support. This role involves diagnosing business challenges, owning AI initiatives, and integrating AI capabilities into existing workflows.

The ideal candidate will possess strong coding skills, an understanding of enterprise systems, and a passion for technology. You will work within a remote and values-driven environment, contributing to GitLab's AI transformation.

Qualifications

• Genuinely invested in technology, both foundational and cutting-edge.
• Able to build working solutions end-to-end and write clean code.
• Understand model selection and cost-performance trade-offs.
• Practical fluency with AI and orchestration tools.
• Design appropriate guardrails and consider safety in solutions.
• Comfortable mapping complex processes and identifying constraints.
• Familiar with enterprise business systems and data workflows.
• Ability to have meaningful conversations with diverse stakeholders.
• Track record of owning complex initiatives and delivering results.
• Ability to scope MVPs and prioritize user experience.

Responsibilities

• Diagnose business problems and determine if AI solutions are needed.
• Own AI initiatives from stakeholder discovery to implementation.
• Design and ship AI-powered solutions quickly for measurable value.
• Build solutions to reduce bottlenecks and measure success metrics.
• Integrate AI capabilities with existing systems and workflows.
• Partner with stakeholders to understand constraints and outcomes.
• Define success through business and flow metrics.

Skills

Technologist at Heart

Competent, Confident Coding Skills

AI Safety & Risk Awareness

Systems Thinking & Diagnostic Rigour

Business System Expertise

Broad Functional Understanding

End-To-End Ownership

Product Mindset

Tools

Salesforce

Marketo

Zendesk

Workato

Relevance AI

Glean

Job description

An overview of this role

As an AI Engineer at GitLab, you’ll help build the foundation for GitLab’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.

Building fast matters, but it’s not enough on its own. This role starts with understanding the real problem: mapping how work moves across teams, tools, and handoffs, identifying the true constraint, and validating whether AI is the right solution before you begin development. From there, you’ll take ownership from discovery through deployment, combining strong engineering skills with systems thinking and business understanding.

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 our mission in a remote, asynchronous, and values‑driven environment.

What you’ll do

• Diagnose business problems before building solutions. Map workflows, identify constraints, and confirm whether AI is the right intervention. Be prepared to say "this doesn’t need AI" when that’s the honest answer.
• Own AI initiatives end‑to‑end, from stakeholder discovery and technical design through implementation, deployment, and iteration.
• Design, develop, and ship AI‑powered solutions quickly, delivering working prototypes in days, not months, with a focus on practical outcomes and measurable business value.
• Improve organizational flow by building solutions that reduce bottlenecks, shorten lead times, and increase throughput. Measure success using flow metrics alongside adoption and ROI.
• Integrate AI capabilities into existing systems and workflows using APIs, orchestration tools, and modern AI platforms, including GitLab Duo Agent Platform, where appropriate. The right tool wins, whether that’s custom code, a platform, or a well‑crafted prompt.
• Be Customer Zero: leverage and showcase GitLab’s AI offerings wherever possible, feeding real‑world usage insights back to R&D.
• 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.
• Define and track success through business metrics, flow metrics, and feedback loops that make performance visible and actionable.
• Contribute to technical direction by evaluating tools, documenting patterns, and creating reusable foundations that help the team scale its impact.

What you’ll bring

• A Technologist at Heart - Genuinely invested in technology, the foundational and the cutting‑edge in equal measure. You’re as energized by a well‑designed API integration as you are by the latest foundation model release. You reach for the simplest solution that solves the problem well, rather than forcing new technology when proven approaches would do. AI is a powerful part of your toolkit, but it sits on top of solid engineering fundamentals, not in place of them.
• Competent, Confident Coding Skills - You can build working solutions end‑to‑end, write clean and maintainable code, and debug effectively. Whether your skills were honed in a traditional engineering role, through building automations, or shipping side projects, what matters is that you can deliver production‑quality work independently.
• 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 versus expanding the context window, and how to make principled decisions about capability versus cost.
• Agentic architecture patterns: tool use, multi‑agent orchestration, human‑in‑the‑loop designs, guardrails, evaluation frameworks, and production‑grade reliability patterns. Practical fluency across the LLM ecosystem: hands‑on experience with models from Anthropic, OpenAI, open‑source alternatives, and the judgment to know which to reach for and when.
• AI Safety & Risk Awareness - You think critically about how the solutions you build could be exploited, misused, or produce unintended consequences. You know how to design appropriate guardrails (input validation, output filtering, access controls, prompt injection defenses, and data leakage prevention) and you treat these as first‑class engineering concerns.
• Systems Thinking & Diagnostic Rigour - The ability to look at a complex process and see the constraint. Comfortable mapping how work flows end‑to‑end, identifying bottlenecks, and tracing problems to root causes before proposing solutions. You instinctively ask "what’s actually blocking flow here?" before asking "what model should I use?"
• Business System Expertise - Familiarity with the landscape of enterprise business systems, CRM (Salesforce), marketing automation (Marketo), support platforms (Zendesk), integration and orchestration tools (Workato), AI platforms (Relevance AI), and enterprise search and knowledge tools (Glean). You don’t need deep experience with all of these, but to understand what they do, how they fit together, and be willing to build with and across them. A strong understanding of enterprise data models and workflows is essential.
• Broad Functional Understanding - Ability to have meaningful conversations with stakeholders across diverse domains and quickly understand their unique needs.
• End‑To‑End Ownership - Track record of owning complex initiatives from discovery through delivery. Comfortable operating with ambiguity and driving to measurable outcomes independently.
• Product Mindset - Ability to scope MVPs, prioritize ruthlessly, and deliver iteratively. In addition, consider adoption, user experience, and business outcomes.

Preferred requirements

• Experience with GitLab platform and CI/CD workflows
• Background in consulting, solutions engineering, or customer‑facing technical roles
• Familiarity with value stream mapping, flow metrics, or Theory of Constraints thinking
• Experience with low‑code/no‑code orchestration tools (n8n, Make, Workato) alongside custom development
• Previous startup or high‑growth company experience
• Experience mentoring or leading technical projects with junior engineers

About the team

You will join the Enterprise Technology & AI team. We’re the backbone of the organization, driving transformation in how GitLab team members make decisions, operate at scale, and deliver results for our customers.

We believe the best AI solutions start with understanding the system, not the technology. We value people who think in constraints and flow, who build with conviction, and who never stop learning. We work in an all‑remote, asynchronous setting, guided by GitLab’s values of collaboration, results, efficiency, diversity, inclusion and belonging, iteration, and transparency.

GitLab is proud to be an equal‑opportunity workplace. GitLab commits to delivering equity and fairness in all aspects of the employee life cycle, and we provide hiring accommodations for individuals with disabilities. GitLab is an affirmative action and equal‑employment opportunity employer, and we do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, or veteran status. All above employment decisions are based solely on merit.