Principal Product Manager, AI Governance

GitLab

13d•Remote

About The Position

As the Principal Product Manager, AI Governance, you'll own GitLab's AI control plane end-to-end — the set of capabilities that give organizations visibility, control, and confidence as they adopt AI across the DevSecOps lifecycle. This spans governance and policy management, admin and access controls, subscription and entitlement management, usage analytics and reporting, audit and compliance, and the platform experiences that make AI trustworthy and manageable at enterprise scale. You'll report to the Vice President of Product for AI and partner closely with engineering, design, security, and cross-functional stakeholders to define outcomes, make tradeoffs, and ship iteratively for large organizations. In your first year, you'll focus on establishing a clear product strategy for AI Governance, translating customer demand into a prioritized roadmap, and delivering foundational capabilities that set teams up for secure, compliant, and scalable use of AI across GitLab. Some examples of our projects: Designing and delivering admin and governance experiences for AI analytics, including controls that support enterprise adoption. Building subscription and entitlement controls that help organizations manage and scale AI analytics usage across teams.

Requirements

Extensive product management experience owning complex, technical products end-to-end — from customer discovery and strategy through roadmap execution and delivery — ideally in B2B SaaS platforms.
Proven experience building governance, admin, or control-plane products for AI, data, or platform capabilities, including policy management, access controls, configuration frameworks, and multi-tenant administration.
Strong understanding of AI product lifecycle and enterprise adoption patterns, including how organizations evaluate, pilot, and scale AI capabilities and the governance requirements that gate each stage.
Experience shipping enterprise readiness capabilities that drive broad adoption, such as admin settings, subscription and seat controls, usage analytics, audit logging, and policy-driven configuration.
Working knowledge of security, compliance, and regulatory concepts as they relate to AI products, including data residency, model governance, access controls, auditability requirements, and emerging AI policy frameworks (e.g., EU AI Act, NIST AI RMF).
Ability to translate complex, technical concepts — model behavior, data flows, policy logic — into clear product requirements, customer-facing value, and internal alignment across cross-functional stakeholders.
Strong customer empathy and experience partnering with enterprise customers, security teams, and compliance stakeholders to understand constraints, validate problems, and prioritize the highest-impact investments.

Responsibilities

Own the product strategy and end-to-end roadmap for AI Governance and the AI control plane, covering policy and access management, subscription and usage controls, analytics and reporting, audit and compliance, and admin configuration — all focused on unlocking broad, confident enterprise adoption of GitLab's AI capabilities.
Define and drive the governance model for AI across GitLab, including hierarchical policy controls, feature-level toggles, role-based access, data-handling settings, and model-selection preferences that give organizations the granularity they need.
Partner closely with engineering, design, security, data, legal, and cross-functional product partners (including AI Platform, Identity, and Fulfillment teams) to define requirements, align dependencies, and deliver a cohesive AI control plane that integrates cleanly across the product.
Translate customer and field feedback into clear problem statements, prioritized outcomes, and pragmatic releases that improve usability, manageability, and trust in AI-powered capabilities at scale.
Establish success metrics for AI Governance — including adoption, activation, policy coverage, and compliance posture — and use data-informed insights to guide prioritization, measure impact, and continuously improve the product.
Operate as a technical, high-autonomy product leader, bringing clarity to ambiguous problem spaces, mentoring other product managers, and representing the governance perspective in broader AI product and platform decisions.