Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI
Communication @ 6
Debugging
Kubernetes @ 4
Load Testing
Performance Analysis @ 4
Software Development @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
GitLab is building an intelligent orchestration platform for DevSecOps. The Performance Enablement team helps engineering teams make performance testing part of the software lifecycle through reusable platforms, tooling, documentation, and artificial intelligence-assisted workflows.
This role focuses on moving from a hands-on consulting model to a scalable self-service model in which service teams own their performance tests, analysis, thresholds, and evidence. You will build backend and platform capabilities that help teams identify performance risks during development and before production releases.
Responsibilities
- Build and evolve reusable backend platforms and tooling for performance testing and analysis.
- Enable modular feature teams, including Orbit and Artifact Registry, to run performance testing through development and continuous integration workflows.
- Create self-service workflows, documentation, templates, diagnostics, and artificial intelligence skills that reduce the need for bespoke support.
- Build reusable tooling for component-level testing, load testing, performance analysis, and artificial intelligence-assisted engineering.
- Improve performance results, reporting, and trend visibility so teams can make informed decisions based on their own evidence.
- Use artificial intelligence to accelerate implementation, testing, debugging, technical documentation, and automation.
- Influence engineering teams to make performance testing part of normal development and release preparation.
- Turn early hands-on engagements into generalized capabilities that teams can adopt independently.
- Provide technical expertise for difficult performance questions, tooling enhancements, and new platform capabilities.
- Establish documentation, templates, and guidance to expand self-service adoption across research and development.
Requirements
- Experience building backend or platform software.
- Experience building internal platforms or developer-facing tooling.
- Experience partnering with development teams to improve their day-to-day workflows.
- Experience applying artificial intelligence across the software development lifecycle.
- Experience with software performance, including performance analysis, optimization, benchmarking, or leading performance-related improvements.
- Practical experience with Kubernetes, k6, and Amazon Web Services, Google Cloud Platform, or similar cloud infrastructure.
- Experience building durable solutions that support broad adoption using systems thinking.
- Clear written communication skills and the ability to influence teams without direct authority.
Team
Performance Enablement is part of Platform Enablement within Developer Experience and Infrastructure Platforms. The team works asynchronously, collaborates broadly across research and development, and focuses on building capabilities that scale beyond individual team engagements.
Compensation
The United States base salary range is $156,800–$235,200 USD. This range does not include bonuses, equity, or benefits. Grade level and salary are determined based on factors including education, experience, knowledge, skills, abilities, internal equity, market data, and geographic location.
Benefits
- Benefits supporting health, finances, and well-being
- Flexible paid time off
- Team member resource groups
- Equity compensation and employee stock purchase plan
- Growth and development fund
- Parental leave
GitLab roles are remote, though some roles may have location-based eligibility requirements. GitLab is an equal opportunity workplace and provides reasonable accommodations during the recruiting process.