Staff AI Engineer, GitLab

• Engineering Experience: 7+ years in software engineering, with recent focus on AI/ML solutions, automation, or platform engineering
• Technical Depth: Strong proficiency in Python and experience with modern AI technologies including LLMs (OpenAI, Anthropic Claude, etc.), agentic orchestration frameworks, and RAG architectures. Comfortable working with APIs, integration patterns, and data management
• Business System Expertise: Hands-on experience integrating with business systems such as CRM (Salesforce), marketing automation (Marketo, HubSpot), support platforms (Zendesk), and financial systems. Understanding of enterprise data models and workflows
• Broad Functional Expertise: Deep understanding of multiple business functions and their processes—spanning Sales, Marketing, Customer Success, Finance, and Operations. 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, defining problems, architecting solutions, and driving to measurable outcomes independently
• Product Mindset: Ability to scope MVPs, prioritize ruthlessly, and deliver iteratively. You think beyond just the code—considering adoption, user experience, and business outcomes
• Analytical & Data-Driven: Strong analytical skills with ability to define KPIs, measure impact, and use data to drive decisions and continuous improvement
• Communication Skills: Excellent written and verbal communication. Can explain complex technical concepts clearly to non-technical stakeholders. Comfortable engaging with senior leaders, gathering requirements from diverse teams, and influencing technical direction
• Bias for Action: Self-starter who takes ownership, moves fast, and isn't afraid to experiment and iterate. Comfortable with ambiguity and building in a fast-paced environment
• Remote Work Excellence: Self-motivated and effective in distributed, asynchronous work environments

Desirable

• As a Staff AI Engineer, you’ll build the foundation for GitLab's transformation into an AI-First company
• Reporting to the Director of AI Engineering, you'll be a hands-on technical leader responsible for the end-to-end delivery of internal AI-powered solutions that drive measurable business outcomes across the organization
• You'll own initiatives from functional requirements through deployment—combining deep technical expertise with broad business acumen to ship fast and drive impact. With initial focus areas in Sales, Marketing, and Customer Support, you'll work across multiple business functions, understanding diverse needs and delivering AI solutions embedded within core systems and workflows
• You'll partner closely with stakeholders across Go-To-Market, R&D, Finance, and other teams to identify high-ROI opportunities and move from concept to production quickly
• Own End-to-End Delivery: Take full ownership of AI initiatives from stakeholder engagement and functional requirements through architecture, development, and deployment. Drive projects to completion with speed and quality
• Build & Ship Fast: Design, develop, and deploy AI-powered solutions that drive measurable business outcomes—with a bias toward rapid iteration and tangible ROI. Initial focus areas include Sales enablement, Marketing automation, and Customer Support optimization
• Platform Integration: Build on top of modern AI platforms and frameworks (including GitLab Duo Agent Platform), integrating AI capabilities into existing business systems via APIs and integration layers
• Be Customer Zero: Leverage and showcase GitLab's AI offerings wherever possible
• Stakeholder Partnership: Taking a consultative approach, work closely with business stakeholders across multiple functions to understand friction points, requirements, and opportunities for AI impact
• Measure Value: Own measurable outcomes tied to business KPIs. Define success metrics, track adoption, and demonstrate ROI that impacts both top and bottom line
• Shape Technical Direction: Contribute to technology selection, architectural decisions, and the long-term technical roadmap. Evaluate new platforms and bring recommendations forward
• Technical Leadership: Set technical standards, document solutions, and create reusable frameworks. As the team grows, provide mentorship and technical guidance to junior engineers
• Stay Ahead: Remain current with AI innovations, evaluate new technologies and platforms, and bring recommendations for consideration