Senior AI Engineer at GitLab

About the Role

As a Senior AI Engineer at GitLab, you will play a key role in building the foundation for GitLab's transformation into an AI-first company. Reporting to the Director, Enterprise AI, you will be a hands-on technical leader responsible for delivering internal AI-powered solutions that yield measurable business outcomes.

This role requires more than just writing code. It begins with deeply understanding the core problem: mapping how work moves across teams and tools, identifying the true bottleneck, and validating if AI is the correct solution before development starts. From initial discovery, you will take end to end ownership, blending strong engineering expertise with systems thinking and business acumen.

Your initial focus will concentrate on Sales, Marketing, and Customer Support functions, where you will embed AI solutions into essential systems and workflows. This position offers a unique chance to shape how GitLab team members operate, improve organizational flow, and advance our mission within a remote, asynchronous, and values-driven framework.

What You Will Do

• Diagnose business problems before building solutions. Map workflows, pinpoint constraints, and confirm if AI is the appropriate intervention. Be ready to state, "this does not require AI" when that is the honest assessment.
• Own AI initiatives from start to finish, including stakeholder discovery, technical design, implementation, deployment, and iteration.
• Design, develop, and deploy AI-powered solutions rapidly, delivering functional prototypes within days, prioritizing practical outcomes and quantifiable business value.
• Enhance organizational flow by constructing solutions that minimize bottlenecks, reduce lead times, and boost throughput. Success will be measured by flow metrics, adoption rates, and ROI.
• Integrate AI capabilities into current systems and workflows using APIs, orchestration tools, and modern AI platforms, including GitLab Duo Agent Platform, where applicable.
• Be the primary user of GitLab's AI offerings, providing real-world usage insights back to the Research and Development team.
• Collaborate closely with stakeholders across various functions to deeply understand real constraints. Ask thoughtful questions, bridge gaps between technical and non-technical viewpoints, and achieve alignment on outcomes prior to solution design.
• Define and monitor success using business metrics, flow metrics, and feedback loops to ensure performance is visible and actionable.
• Contribute to the technical vision by evaluating tools, documenting patterns, and establishing reusable foundations to scale the team's impact.

What You Should Bring

A Technologist at Heart

You must be genuinely invested in technology, encompassing both foundational and cutting edge concepts. You appreciate the elegance of a well designed API integration as much as the latest foundation model release. You favor the simplest solution that solves the problem effectively, avoiding the forced application of new technology when proven methods suffice. AI is a potent tool in your arsenal, but it operates upon solid engineering principles, not in replacement of them.

Competent and Confident Coding Skills

You must be able to build complete, working solutions, write clean and maintainable code, and debug issues effectively. Whether your skills developed in a traditional engineering role, through building personal automations, or shipping side projects, the key is your ability to deliver production quality work autonomously.

AI and LLM Technical Depth

• Prompt Engineering as a Core Discipline: Deep skill in designing effective system prompts, managing context windows, structuring multi turn interactions, assessing output quality, and systematically improving prompt designs.
• Model Selection and Cost Performance Trade-offs: Understanding when a smaller fine tuned model surpasses a general purpose large one, when Retrieval Augmented Generation (RAG) is the correct architecture versus expanding the context window, and how to make principled decisions about capability versus cost.
• Agentic Architecture Patterns: Hands on experience with tool use, multi agent orchestration, human in the loop designs, guardrails, evaluation frameworks, and patterns ensuring production grade reliability.
• Practical Fluency Across the LLM Ecosystem: Hands on experience with models from Anthropic, OpenAI, open source alternatives, and the wisdom to choose the right model for the job.

AI Safety and Risk Awareness

You think critically about potential exploitation, misuse, or unintended outcomes of your solutions. You know how to implement appropriate guardrails, such as input validation, output filtering, access controls, prompt injection defenses, and data leakage prevention, treating these as critical engineering concerns.

Systems Thinking and Diagnostic Rigor

The ability to view a complex process and locate the constraint. You are comfortable mapping end to end workflows, identifying bottlenecks, and tracing problems to their root causes before suggesting solutions. Your instinct is always to ask "what is actually blocking flow here?" before asking "what model should I use?"

Business System Expertise

Familiarity with the ecosystem of enterprise business systems, including CRM like Salesforce, marketing automation such as Marketo, support platforms like Zendesk, integration and orchestration tools like Workato, AI platforms such as Relevance AI, and enterprise search and knowledge tools like Glean. While not required to be an expert in all, understanding their function and how they interconnect is essential.

Broad Functional Understanding

Capacity to engage in meaningful discussions with stakeholders from diverse domains and quickly grasp their specific needs.

End to End Ownership

A proven track record of owning complex projects from discovery through final delivery, comfortable operating amid ambiguity and driving towards measurable results independently.

Product Mindset

Ability to scope Minimum Viable Products (MVPs), prioritize ruthlessly, and iterate effectively while considering adoption, user experience, and overall business results.

Preferred Requirements

• Experience with the 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 like n8n, Make, or Workato alongside custom development.
• Previous experience in a startup or high growth company.
• Experience mentoring or leading technical projects involving junior engineers.

About the Team

You will be joining the Enterprise Technology & AI team. This team is the organizational backbone, driving transformation in how GitLab team members make decisions, operate at scale, and deliver exceptional results for our customers. We strongly believe that the best AI solutions originate from understanding the system, not just the technology. We value individuals who think about constraints and flow, build with conviction, and commit to perpetual learning. We operate in a fully remote, asynchronous setting, guided by GitLab's core values: collaboration, results, efficiency, diversity, inclusion, belonging, iteration, and transparency.

Compensation and Benefits

The base salary range listed for this role's grade level is currently specified for residents of the United States only. This range reflects the base salary rates in various locations throughout the US. Grade level and salary bands are finalized through interviews and a comprehensive review of the applicant's education, experience, knowledge, skills, abilities, equitable standing with team members, alignment with market data, and geographic location.

The stated base salary range does not encompass any bonuses, equity, or benefits. For comprehensive details, please see the benefits and equity sections. Sales roles are also eligible for incentive pay structured to reach up to 100% of the offered base salary.

United States Salary Range: $108,400 - $129,600 USD

How GitLab Supports Full Time Employees

• Benefits designed to support your health, finances, and overall well being.
• Flexible Paid Time Off.
• Team Member Resource Groups.
• Equity Compensation and Employee Stock Purchase Plan.
• Growth and Development Fund.
• Parental Leave.

GitLab welcomes applications from candidates across all experience levels. We recognize that many successful applicants may not meet every single criterion. We encourage you to apply, as studies indicate people from underrepresented groups are less likely to apply unless they meet every single qualification. Allow our recruiters to assess your unique profile.

Hiring Guidelines

Country Hiring Guidelines: GitLab hires team members globally. While all our roles are remote, some positions may have specific location based eligibility requirements. Our Talent Acquisition team is available to clarify location specifics after the recruitment process commences. Review our Recruitment Privacy Policy. GitLab is proud to maintain an equal opportunity workplace and is an affirmative action employer. All recruitment, employment, career development, advancement, promotion, and retirement practices are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab strictly prohibits discrimination or harassment based on any of these characteristics. Consult GitLab's EEO Policy. If you require a disability or special need accommodation, please inform us during the recruiting process.