Staff Backend Engineer, Knowledge Graph (Rust) at 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 a Staff Backend Engineer on the GitLab Knowledge Graph team, you'll help design, scale, and operate a high‑impact graph data service that underpins agents, analytics, and architecture‑level features across GitLab.com, Dedicated, and Self‑Managed deployments. You'll partner with a small, senior Rust‑first team to ship reliable graph capabilities and make them easy for other teams and agents to use. The Knowledge Graph service is a distributed SDLC indexing system. It builds a property graph from GitLab SDLC (software development lifecycle) and code data using ClickHouse, NATS JetStream, and the Data Insights Platform. It also exposes secure graph queries and MCP tools for AI agents and product features. In this role, you’ll own core parts of the system end to end: shaping the architecture, hardening multi‑tenant behavior and performance, and making it straightforward for other teams and agents to consume graph capabilities. In your first year, you’ll take clear ownership of major areas of the service (for example, the graph query engine, SDLC indexing, or multi‑tenant authorization), reduce single points of failure through better runbooks and shared context, and raise the bar on how we design, build, and operate analytical services across the stack. What you’ll do • Lead the design and evolution of core Knowledge Graph services in a production Rust codebase, including the graph query engine, SDLC and code indexing pipelines, and API/MCP surfaces that other GitLab teams and AI agents rely on. • Own complex, cross-cutting initiatives that span GitLab Rails, the Data Insights Platform (Siphon, NATS, ClickHouse), and GitLab Duo Agent Platform, from technical direction and design docs through implementation, rollout, and iteration. • Drive system design decisions that improve reliability, scalability, and maintainability for analytical (OLAP‑style) graph workloads. This includes multi‑hop traversals, aggregations, and multi‑tenant isolation. Document trade‑offs so the broader team can move quickly and stay aligned. • Define and improve operational maturity for the service, including service level objectives (SLOs), observability, runbooks, incident response, capacity planning, and production readiness (PREP) for GitLab.com, Dedicated, and Self-Managed deployments. • Collaborate asynchronously with product, data, infrastructure, security, and AI teams to sequence work, unblock platform‑level dependencies, and land features in a way that is safe for customers and sustainable for the team. • Apply AI‑assisted development workflows responsibly (for example, using MCP‑aware tools, Knowledge Graph-backed agents, and internal Duo tooling) and help establish practical norms for how the team uses AI while maintaining strong engineering judgment. • Mentor and support other engineers through pairing, technical design reviews, and knowledge-sharing, reinforcing shared ownership of the system and its operational sustainability. • Contribute across the stack when needed, including occasional Ruby (Rails integration and authorization paths) or frontend work (for example, the Software Architecture Map UI) to close gaps and keep delivery moving. What you’ll bring • Significant experience building and operating production backend systems, with a track record of owning reliability, maintainability, and on-call readiness for services that support other product teams or platforms. • Strong engineering skills in Rust or clear evidence you can ramp quickly and deliver in a Rust-first, performance-sensitive backend codebase. • Strong system design skills, including making and explaining clear architectural decisions, documenting constraints, and aligning trade-offs with product and platform needs. • Strong fundamentals in preparing and structuring information for AI agents: how to curate and organize what the agent sees, design systems that support effective LLM-powered behavior, and manage context windows and token usage. • Comfort working in ambiguous environments, with the ability to work autonomously and stay self-directed. Demonstrated ability to identify problems, drive solutions, and take ownership. • Experience with distributed data or analytics systems (for example, OLAP databases like ClickHouse or columnar stores, Kafka‑ or NATS‑style messaging, or change data capture (CDC) pipelines), and comfort reasoning about trade‑offs in that space. • Familiarity with graph data modeling and/or query patterns (property graphs, Cypher/GQL, n-hop traversals, aggregations), or strong interest in developing that expertise in this role. • Practical experience using AI tools in day-to-day development, with the ability to explain how you structure prompts, validate outputs, and fold AI assistance into a disciplined engineering workflow. • A language-agnostic mindset and evidence that you can learn new languages and frameworks as the problem requires (for example, Ruby, Go, or TypeScript/Vue in adjacent parts of the stack). • Excellent written communication and comfort collaborating asynchronously across teams and time zones in an all-remote environment. • Interest in helping grow others through mentoring, thoughtful code review, and sharing context as the team scales and more customers adopt Knowledge Graph. The Knowledge Graph team sits within the data engineering organization and builds the backend service that turns GitLab's SDLC and code data into a unified property graph. We expose it through a high-performance, ClickHouse-backed query engine and MCP tools. We're a small group of senior engineers working closely with partners across AI (Duo Agent Platform), analytics, infrastructure, delivery, and security because our work touches many layers of the platform. We work asynchronously and value strong ownership: each engineer is expected to build a deep understanding of the system and help evolve it over time. As we grow adoption, we're focused on scaling the service sustainably and making it reliable and easy to operate for GitLab.com, Dedicated, and Self-Managed customers. How GitLab will support you • 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. 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.