Backend Engineer, Knowledge Graph (Rust)
GitLab · Mumbai Metropolitan Region
- Hybrid
- Internship
- $150,000 / year
- Mumbai Metropolitan Region
Job highlights
- Build and operate a Rust-based graph data service.
- Work with ClickHouse, NATS JetStream, and AI agents.
- Develop backend features and improve system reliability.
- Collaborate asynchronously in a high-ownership environment.
- Contribute to scaling and operational maturity efforts.
About the role
About GitLab
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 an Intermediate Backend Engineer on the GitLab Knowledge Graph team, you'll help build and operate a graph data service that supports GitLab Duo agents, analytics, and architecture-level features across GitLab.com, Dedicated, and Self-Managed deployments. You’ll join a small, Rust-first team that values clear ownership, thoughtful system design, and rigorous thinking about data and reliability.The Knowledge Graph service is a Rust backend that builds a property graph from GitLab’s software development lifecycle (SDLC) and code data. It uses ClickHouse, NATS JetStream, and the Data Insights Platform. It exposes secure graph queries and MCP tools used by AI agents and product features. In this role, you’ll deliver features and improvements in well-scoped areas, learn the broader architecture, and contribute to reliability, observability, and operational readiness.
In your first year, you’ll take clear ownership of specific components or features (for example, parts of the SDLC indexing pipeline or query paths). You’ll help reduce single points of failure with better tests and runbooks, and you’ll help the team ship analytical services that are easier to maintain and evolve over time.
What you’ll do
- Implement and iterate on backend features in the Rust-based Knowledge Graph service, including changes to the query engine, SDLC and code indexing flows, and API endpoints (including MCP endpoints) under guidance from senior and staff engineers.
- Help maintain integrations between Knowledge Graph and the rest of the GitLab platform, working in areas that touch GitLab Rails, the Data Insights Platform (Siphon, NATS, ClickHouse), and GitLab Duo Agent Platform.
- Contribute to system design discussions by proposing options, raising questions, and documenting decisions, with a focus on reliability, scalability, and maintainability for analytical graph workloads.
- Improve the operational maturity of the service by adding or enhancing metrics, logging, runbooks, alerts, and small readiness tasks, and by participating in on-call rotation as appropriate for your level and experience.
- Collaborate asynchronously with product, data, infrastructure, security, and AI counterparts to clarify requirements, align on scope, and ship features safely for customers and sustainably for the team.
- Use AI-assisted development workflows responsibly (for example, using Knowledge Graph-backed agents and internal Duo tooling), and share what works with the team while keeping a strong focus on code quality and correctness.
- Participate in code reviews, knowledge-sharing sessions, and pairing to both learn from others and help maintain consistent standards across the codebase.
- Contribute across the stack when needed, including occasional Ruby work for Rails integration and authorization paths, or small frontend changes related to Knowledge Graph features (for example, Software Architecture Map UI plumbing).
What you’ll bring
- Professional experience building and maintaining backend systems in production, with an understanding of reliability, maintainability, and how to support services over time (incident responses, and follow-ups, etc).
- Proficiency in at least one modern backend language and strong interest in Rust, with either prior Rust experience or clear evidence you can ramp quickly and deliver in a Rust-first, performance-sensitive codebase.
- Some exposure to distributed data or analytics systems (for example, OLAP databases, Kafka- or NATS-style messaging, or change data capture (CDC) pipelines), or strong motivation to develop those skills in this role.
- Interest in graph data modeling and query patterns (property graphs, multi-step (n-hop) traversals, aggregations), and willingness to learn the tools and concepts used in Knowledge Graph over time.
- Practical experience (or strong interest) using AI tools in day-to-day development, along with a thoughtful approach to validating outputs and integrating AI into your workflow.
- A language-agnostic mindset and evidence that you can pick up new languages and frameworks as needed (for example, Ruby, Go, or TypeScript/Vue where the work touches adjacent systems).
- Solid fundamentals in system design for your level, including the ability to reason about trade-offs, ask good questions, and align your implementation work with documented architectural decisions.
- Comfort working in a low-process, high-ownership environment where you take responsibility for your work, communicate progress clearly, and help refine problem statements with your teammates.
- Strong written communication and comfort collaborating asynchronously across time zones in an all-remote team.
About The Team
We sit within the Data Engineering organization. We're a small group of senior engineers and we work closely with partners across AI (Duo Agent Platform), analytics, infrastructure and delivery, and security because our work spans many parts of the platform.We collaborate asynchronously and optimize for strong ownership rather than a feature factory model. We each build a meaningful understanding of the system and help evolve it over time. A key challenge for us right now is scaling sustainably. That includes hardening multi-tenant behavior, maturing observability and readiness, and keeping the system healthy and maintainable as usage grows and team members take time off. At the end of the day, we're bringing Knowledge Graph to general availability (GA).
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
- Home office support
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.
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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.
Key skills/competency
- Rust
- Backend Development
- Graph Databases
- Data Engineering
- Distributed Systems
- System Design
- API Development
- Observability
- Reliability Engineering
- AI Integration
Skills & topics
- Backend Engineer
- Rust
- Knowledge Graph
- Graph Databases
- Data Engineering
- Distributed Systems
- System Design
- API Development
- DevOps
- Software Development
How to get hired
- Tailor your resume: Highlight backend experience, Rust proficiency, and graph/data systems knowledge.
- Showcase Rust skills: Emphasize any Rust projects or your ability to learn quickly.
- Demonstrate problem-solving: Provide examples of maintaining production systems and handling incidents.
- Highlight collaboration: Detail your experience with asynchronous communication and remote teams.
- Express interest in AI: Mention any experience or enthusiasm for AI-assisted development.
Technical preparation
Study Rust programming language fundamentals.,Practice graph data modeling and query patterns.,Familiarize with ClickHouse and NATS.,Review system design principles for distributed systems.
Behavioral questions
Describe a time you owned a system component.,How do you handle incident response and follow-ups?,Share your experience with asynchronous collaboration.,Discuss your approach to using AI in development.
Frequently asked questions
- What is the GitLab Knowledge Graph team focused on?
- The GitLab Knowledge Graph team builds and operates a Rust-based graph data service that supports AI agents, analytics, and various platform features. They focus on data reliability, scalability, and operational maturity.
- What technologies are used in the Knowledge Graph service?
- The service primarily uses Rust and integrates with ClickHouse, NATS JetStream, and the Data Insights Platform. Some Ruby experience may also be needed for integrations.
- What is expected of an Intermediate Backend Engineer in this role?
- You will implement backend features, maintain integrations, contribute to system design, improve operational maturity, and collaborate with cross-functional teams, taking ownership of specific components.
- Does GitLab expect prior Rust experience for this role?
- While proficiency in Rust is a strong plus, GitLab is open to candidates with strong modern backend language skills and a demonstrated ability to ramp up quickly in a performance-sensitive, Rust-first environment.
- What kind of work arrangement does GitLab offer for this position?
- This is a fully remote position, with GitLab hiring team members worldwide. However, some roles may have specific location-based eligibility requirements.
- How does GitLab encourage continuous learning and development?
- GitLab offers a Growth and Development Fund, encourages knowledge exchange, and provides opportunities to learn from senior engineers, fostering career acceleration and innovation.
- What is the team's approach to collaboration and work style?
- The team collaborates asynchronously, values strong ownership, and focuses on thoughtful system design and rigorous thinking about data and reliability. They aim for sustainable scaling and operational maturity.
- Are there opportunities to work with AI in this role?
- Yes, the role involves using and integrating with AI-assisted development workflows, including Knowledge Graph-backed agents and internal Duo tooling, to drive efficiency and innovation.