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 Staff Product Manager, Test & Code Coverage, you'll build product capabilities from initial discovery through launch and adoption. You'll help engineering teams understand why pipelines fail, improve test automation, and use code coverage to make better decisions about software quality and risk.
You'll turn a technically complex problem space into a focused strategy, roadmap, and developer experience. Working closely with Engineering and Design, you'll explore how agents can diagnose failures, recommend next steps, and help teams recover safely. The goal isn't to add artificial intelligence (AI) for its own sake. It's to reduce manual work and help developers get back to a safe, shippable state without losing trust or control.
At GitLab, our mission is simple: to enable everyone to contribute to and co-create the software that powers our world. You'll contribute to that mission by making testing and coverage insights more useful across the software development lifecycle.
What you'll do
- Define and communicate the Test & Code Coverage product vision, strategy, and outcome-focused roadmap, from discovery through a minimum viable product, beta, launch, and continued iteration.
- Identify customer problems across pipeline failure diagnosis, test automation, test optimization, code coverage, and recovery. Build the case for investment, prioritize foundational and customer-facing work, and reshape or stop initiatives that aren't creating value.
- Work with Engineering to understand pipeline failures caused by flaky tests, testing gaps, environment issues, configuration problems, and coverage blind spots. Connect code changes, tests, pipeline behavior, historical patterns, and risk to help developers find a diagnosis and an actionable next step.
- Work directly with customers to understand how they author, maintain, select, prioritize, and debug tests. Connect test selection, test impact analysis, code coverage, and pipeline intelligence to reduce flaky tests, shorten feedback loops, and focus testing effort where it lowers the most risk.
- Partner with Engineering and Design to prototype agent-driven workflows for failure diagnosis, test authoring and repair, optimization, coverage analysis, and recovery. Make actions reviewable through evidence, confidence, human approval, and safe paths to propose code changes and merge requests. Use AI tools in your own product work to investigate problems, explore code and data, prototype concepts, and accelerate learning.
- Define and measure outcomes such as time to diagnose, time to recover, failure recurrence, test signal quality, and developer confidence. Validate problems and solutions with customers and learn from production usage.
- Partner across Product, Engineering, Design, Quality, Developer Experience, Customer Success, Support, and Product Marketing to connect testing insights with code review, pipelines, and release decisions. Turn customer evidence into clear narratives that support adoption and future investment.
- Lead through clear written communication, prioritization, and product judgment. Influence senior stakeholders, establish repeatable discovery and measurement practices, and mentor other product managers.
What you'll bring
- Experience owning complex technical products, platforms, or developer workflows at Staff, Senior Staff, Principal, or equivalent scope, including building a new product or capability from initial validation through launch and adoption.
- Strong product fundamentals in customer discovery, problem framing, prioritization, roadmap development, outcome measurement, and iterative delivery.
- Technical fluency in one or more relevant areas, such as software testing, test automation, code coverage, continuous integration and continuous delivery (CI/CD), developer tools, DevOps, developer experience, quality engineering, or software delivery.
- An understanding of how engineering teams build, test, review, and ship software, and the ability to work with engineers on test architecture, pipeline behavior, code changes, data, integrations, reliability, and scale.
- Experience diagnosing technical problems and turning complex, noisy signals into clear user experiences and actionable workflows.
- Experience exploring AI or agent-driven solutions for developer workflows, with the judgment to distinguish useful automation from unsafe or low-confidence actions.
- Experience using customer conversations to understand problems and inform product decisions, communicating clearly in writing and conversation, and influencing across functions with shared ownership. You can work directly with developers, quality engineers, platform teams, engineering leaders, and executives, and move between strategy and implementation detail.
About the team
Our work brings together Product, Engineering, and Design, with input from Quality, Developer Experience, Customer Success, Support, and Product Marketing. We work directly with developers, quality engineers, and engineering leaders to understand their testing practices, validate ideas, and learn from how they use the product.
We give Engineering and Design clear customer context and room to shape solutions. Our decisions draw on customer evidence and measurable outcomes, and we revisit approaches that aren't creating value. You'll contribute to how we discover problems, prioritize work, and measure progress, as well as sharing product management practices with other product managers.
As part of GitLab's all-remote organization, we collaborate asynchronously across regions. Our work emphasizes transparency, written communication, and clear decision-making.
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
$168,000 - $285,600 USD
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.
Salary Context
This $168K-$285K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($226K) sits 7% above the category median. Disclosed range: $168K to $285K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
GitLab Inc AI Hiring
GitLab Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in Remote, US. Compensation range: $285K - $285K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
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