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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.
Co-create the future with us as we build technology that transforms how the world develops
software.
*Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim
based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune
500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with,
and do not endorse products or services of GitLab.An overview of this role
As an AI Transformation Owner at GitLab, you'll shape your function's AI strategy and
build the solutions that deliver it. You are the person responsible for identifying where AI
can fundamentally change how your org operates, partnering with your Executive Sponsor to align
on the biggest challenges, and driving measurable outcomes against them.
Think of this as a product management role where the product is your org's way of working.
You'll manage the full lifecycle: understanding how work flows today, deciding where AI
should reshape it, prioritizing what gets built and in what order, and ensuring what ships
actually gets adopted. You'll also prototype solutions, configure agents, and prove
what's possible before pulling in engineering support to scale it.
You will work closely with an AI Engineer who sits within the Enterprise AI team. Together you
form a partnership: you bring the business context, process intelligence, and strategic
prioritization. They bring the technical depth, production-grade delivery, and architecture
decisions. You'll build working solutions at the no-code and low-code layer, and partner
with the AI Engineer on the right approach, tooling, and structure.
What you'll do
Strategy & Prioritization

Own your function's AI strategy, aligned with your Executive Sponsor and business
priorities. Understand which metrics matter to the org, identify what will move the needle,
define how you'll measure impact, and track progress over time.
Map how work flows across your function end-to-end, including the handoffs upstream and
downstream to other orgs. Identify where the real constraints are, not just the ones your team
can see. Focus on the 100x problems: where could leveraging AI in a workflow let your org
execute it orders of magnitude faster, or at 100x more volume than before?
Manage intake of AI requests, ideas, and pain points from across the function, including via
your Champion network. Ensure every team member has a clear route to surface what they need,
rather than building independently.
Prioritize strategically against business outcomes and executive guidance. Hold the line on
priorities - we cannot change direction every two weeks - and ensure the AI Engineer's time
is spent on the highest-impact work.

Adoption & Change Management

Reimagine, not just automate. Challenge your org to think beyond injecting AI into existing
workflows. Work with Enterprise AI to spot opportunities to fundamentally rethink how work gets
done.
Drive adoption and change management together with the AI Engineer. The best AI solution is
worthless if nobody uses it. Create the channels, rituals, and feedback loops that make AI
visible in your function: shared spaces for teams to show what they've built, regular
office hours, onboarding for new hires, and celebration of wins. Own the rollout and iteration
needed to make AI initiatives stick.
Coordinate with Enterprise AI to ensure your function benefits from patterns, tools, and
learnings emerging across other parts of the business.
Build and bridge the Champion network in your function. Champions are the peer community that
extends your reach beyond what you and the AI Engineer can deliver directly. From early in the
role, identify and recruit Champions across sub-teams (5-10% time, formally agreed with their
manager), run a regular Champion sync, host demos to the wider function, and act as their
bridge to Enterprise AI. Champions are not your reports: you coordinate them, you don't
manage them. Without this network, your reach is capped.

Hands-On Building & Agent Operations

Build AI agents using no-code and low-code platforms (e.g. Glean, Workato, similar tools). Go
from idea to working prototype without waiting for engineering.
Author and iterate on skills files that define how AI agents behave. Refine instructions based
on real usage and share reusable skills across the function.
Configure MCP servers and tools, giving agents access to the business systems they need.
Partner with the AI Engineer on what to connect and how to do it securely.
Own your function's fleet of agents. Some agents will be used directly by people in your
org. Those that aren't, you own. Either way, you're accountable for their performance:
tracking KPIs, running evaluations after model or data changes, and iterating based on what you
learn.
Expect to rebuild. AI tools and models evolve fast. The agent you built last month may need to
be replaced, not patched. You should be comfortable sunsetting your own work when a better
approach emerges, and helping your org stay current rather than attached to what exists today.

What you'll bring
Business & Strategic

Deep knowledge of your function's operations. You understand how work moves through your
org, where it gets stuck, and why. You can trace a process end-to-end and explain how it
connects to the teams around it.
Strategic prioritization skills. You've managed competing demands before and can make hard
calls about what matters most. You think in terms of business outcomes, not activity.
A product management mindset. You naturally think about intake, backlog, iteration, and
adoption. You're comfortable defining success metrics and holding yourself accountable to
them.
Strong communication and influence. You'll be the person saying "not yet" to some teams and
"think bigger" to others. You need the credibility and interpersonal skills to make both of
those conversations land.
Cross-functional instincts. You default to understanding how your org's processes affect
and are affected by the teams around you - not just optimising in isolation.
Experience building peer networks or communities of practice. You've recruited and
sustained volunteer contributors before, whether as a guild lead, champion program owner, or
community organizer. You know how to motivate people whose time you don't directly own.

Hands-On & Technical

Comfortable building with AI tools. You don't need to write production code, but you should
be able to build a working agent, configure a skill, connect an MCP server, and troubleshoot
when something isn't working. Think: power user, not software engineer.
Ready to learn fast. Experience with or willingness to quickly pick up no-code/low-code AI
platforms, prompt engineering, and agent configuration. You'll be trained on GitLab's
specific tooling, but you should arrive ready to get your hands dirty.
Strong conceptual understanding of AI capabilities - summarization, classification, generation,
automation, agentic workflows - and a commitment to staying current. The landscape shifts
constantly. The tools you use today may be obsolete in weeks. You stay on top of new
developments so that your org's AI strategy reflects what's actually possible, not what
was possible six months ago.
Ability to map data flows - structured and unstructured. Understand where agents need context,
and figure out where humans should interface with automated workflows and at what steps.

About the team
You will partner closely with Enterprise AI within the Enterprise Technology & AI
organization, while remaining embedded in your own function. Enterprise AI provides the
technical delivery capability, platforms, and patterns. You provide the business context,
prioritization, building at the no-code layer, and adoption muscle. Together, you form the core
of your function's AI transformation.The base salary range for this role’s listed level
is currently for residents of the United States only. This range is intended to reflect
the role's base salary rate in locations throughout the US. Grade level and salary
ranges are determined through interviews and a review of education, experience, knowledge,
skills, abilities of the applicant, equity with other team members, alignment with market
data, and geographic location. The base salary range does not include any bonuses,
equity, or benefits. See more information on our benefits and equity. Sales roles are also
eligible for incentive pay targeted at up to 100% of the offered base salary.United States
Salary Range$203,200—$345,600 USDHow GitLab Supports Full-Time Employees

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

Please note that we welcome interest from candidates with varying levels of experience; many
successful candidates do not meet every single requirement. Additionally, studies have shown
that people from underrepresented groups are less likely to apply to a job unless they meet
every single qualification. If you're excited about this role, please apply and allow our
recruiters to assess your application.

Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of
our roles are remote, however some roles may carry specific location-based eligibility
requirements. Our Talent Acquisition team can help answer any questions about location after
starting the recruiting process.  
Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us.
GitLab is proud to be an equal opportunity workplace and is an affirmative action employer.
GitLab’s policies and practices relating to recruitment, employment, career development and
advancement, promotion, and retirement are based solely on merit, regardless of race, color,
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veterans), or any other basis protected by law. GitLab will not tolerate discrimination or
harassment based on any of these characteristics. See also GitLab’s EEO
Policy and EEO is the Law. If you have a disability or special need that
requires accommodation, please let us know during the recruiting process.