Interested in this AI/ML Engineer role at GitLab Inc?
Apply Now →About This Role
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. 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 a Senior Manager of Product Management, you will lead one of GitLab's most strategic product portfolios: Agentic Software Delivery, spanning Continuous Integration, Artifact Management, Continuous Deployment, and Observability. This is a people leadership role. You will guide senior, domain\-expert product managers and define how these connected areas evolve as AI agents take on more of the execution work in building, testing, shipping, and monitoring software.
Today these are treated as separate stages with a human making the call at each handoff. That model is changing. As agents write more of the code and take on more of the pipeline and deployment work, the real product challenge shifts to how much of that judgment the system can make on its own, safely, and how quickly a production signal turns into a fix without waiting on a human to notice it first. You will own the product vision for that shift and for how customers move from today's manual checkpoints toward more agent\-driven ones, at whatever pace fits their risk tolerance.
In your first year, you will set direction across this portfolio, strengthen alignment with engineering and go to market partners, and give GitLab customers a clearer path for managing software delivery at scale. This role suits a product leader who can connect technical strategy, commercial outcomes, and team development while influencing across a broad part of GitLab's platform.
What you’ll do
------------------
- Lead a team of senior and principal product managers across Continuous Integration, Artifact Management, Continuous Deployment, and Observability, providing strategic direction, coaching, and support.
- Define the product strategy and long\-term vision for GitLab's Agentic Software Delivery portfolio, with a clear point of view on how AI is changing software delivery workflows.
- Connect portfolio decisions across build, package, deploy, and monitor experiences so the customer journey is coherent across the platform.
- Collaborate with engineering leadership on platform investments, architectural direction, and trade\-offs across the portfolio.
- Drive pricing, packaging, and go\-to\-market alignment with product marketing, sales, pricing, customer success, and solutions engineering to support commercial growth across the portfolio.
- Use customer insight and product data, including usage telemetry, retention signals, and adoption patterns, to guide roadmap prioritization and improve product adoption and retention.
- Champion migration and adoption paths that help existing customers move from current workflows to more advanced CI/CD and AI\-enabled delivery models, increasing adoption across the platform.
- Model AI\-native product management by using AI tools for discovery, synthesis, prototyping, roadmap exploration, and product concept development, while guiding the team to do the same.
What you’ll bring
---------------------
- Experience leading product managers in a people leadership role, including setting direction, mentoring senior talent, and maintaining a high bar for product management.
- Hands\-on product management experience in one or more relevant domains such as CI/CD pipelines, artifact or package management, release management, or observability.
- Ability to own a broad portfolio across multiple product areas and align teams around a shared product vision and execution model.
- Technical fluency to work credibly with engineers and architects on topics such as pipeline design, artifact workflows, deployment strategies, and observability signals like logs, metrics, and traces.
- Experience working with technical customers in developer tools, DevOps, or software delivery environments where engineers and engineering leaders are primary users.
- Comfort with pricing and packaging decisions for technical products, including working across free, commercial, and enterprise offerings.
- Strong communication and influence skills, with the ability to align executives, cross\-functional partners, and customers around a clear strategy.
- Practical use of AI tools in product management workflows, along with a thoughtful view on how AI and agentic workflows are changing software delivery. We welcome candidates whose experience may come from different but transferable product and leadership paths.
About the team
------------------
We bring together senior product leaders across GitLab’s software delivery platform and work closely with engineering, design, and go\-to\-market partners to ensure customers experience these capabilities as one connected system. We help customers manage today’s delivery complexity while shaping how AI\-powered and AI\-native workflows influence the next generation of software delivery. As part of GitLab’s all\-remote organization, we collaborate asynchronously across regions with a strong focus on transparency, written communication, and clear decision\-making.
#### How 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, 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 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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At GitLab Inc, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
GitLab Inc AI Hiring
GitLab Inc has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Engineering Manager. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.