AI Engineer

Details

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.

Responsibilities

• Diagnose business problems before building solutions: map workflows, identify constraints, and confirm whether AI is the right intervention.
• 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 with 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).
• Act as Customer Zero: leverage and showcase GitLab's AI offerings and feed real-world usage insights back to R&D.
• Partner with stakeholders across functions to understand constraints, bridge technical and non-technical perspectives, and align on outcomes.
• Define and track success through business metrics, flow metrics, and feedback loops.
• Contribute to technical direction by evaluating tools, documenting patterns, and creating reusable foundations.

Requirements

• Technologist mindset: invested in both foundational and cutting-edge technology; prefer simplest effective solutions.
• Competent, confident coding skills: able to build end-to-end production-quality solutions independently.
• Strong proficiency in at least one modern scripting language (Python, JavaScript/TypeScript, or similar).
• Solid understanding of REST APIs, GraphQL, and integration patterns.
• Deep, practical experience with modern AI technologies, specifically:
  • Prompt engineering as a core discipline: designing effective system prompts, managing context windows, structuring multi-turn interactions, evaluating output quality, and iterating on prompt design.
  • Model selection and cost-performance trade-offs: when to use smaller fine-tuned models vs. general-purpose large models, when to use RAG vs. expanding context windows.
  • 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, including models from Anthropic, OpenAI, and open-source alternatives.
• AI safety & risk awareness: design guardrails (input validation, output filtering, access controls, prompt injection defenses, and data leakage prevention) as first-class engineering concerns.
• Systems thinking & diagnostic rigour: map end-to-end processes, identify bottlenecks, and trace root causes before proposing solutions.
• Familiarity with enterprise business systems and tools (CRM like Salesforce, marketing automation like Marketo, support platforms like Zendesk, integration/orchestration tools like Workato, AI platforms like Relevance AI, enterprise search/knowledge tools like Glean).
• End-to-end ownership: track record of owning complex initiatives from discovery through delivery.
• Product mindset: scope MVPs, prioritise ruthlessly, deliver iteratively, and consider adoption and user experience.

Preferred

• 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.
• 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, focused on driving transformation in how GitLab team members make decisions, operate at scale, and deliver results. The team works in an all-remote, asynchronous setting and values collaboration, results, efficiency, diversity, inclusion, iteration, and transparency.

Compensation & Benefits

• United States Salary Range: $108,400 - $129,600 USD (base salary range for U.S. residents; does not include bonuses, equity, or benefits).
• Benefits: links to GitLab benefits, flexible paid time off, team member resource groups, equity compensation & employee stock purchase plan, growth and development fund, parental leave.

Additional notes

• GitLab hires new team members in countries around the world. Roles are remote but may carry location-based eligibility requirements. Talent Acquisition can answer location questions.
• GitLab is an equal opportunity employer and provides accommodation during the recruiting process where needed.