Backend Engineer, Knowledge Graph (Rust)

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Remote from: USA, Canada
Salary: USD 115,200–162k / yr
Department: Software Engineering
Employment: Full Time
Experience: Senior
Published: 26 Oct 2026
Apply before: 25 Nov 2026
Listing views: 20
Application actions: 2

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AI Summary

The role, at a glance.

GitLab is hiring an intermediate Backend Engineer to build and operate its Rust-based Knowledge Graph service. The role focuses on graph query capabilities, SDLC and code indexing pipelines, APIs including MCP endpoints, and integrations with Rails, ClickHouse, NATS JetStream, and GitLab's data platform. The engineer will improve reliability through testing, observability, runbooks, alerts, and appropriate on-call participation. This is a high-ownership, asynchronous remote role requiring strong written collaboration across product, infrastructure, data, security, and AI teams.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5

EasyHard

Pace & Pressure

4/5

RelaxedFast-paced

Autonomy Level

4/5

GuidedFull ownership

Communication Load

5/5

IndependentCollaborative

AI insightThe role combines production Rust development with distributed data systems, graph modeling, operational reliability, and cross-platform integrations. It is intermediate in level but requires sound architectural judgment and the ability to independently own well-defined components.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate

$138,600

US market range (per year)$115k–$165k

0$181.50k

AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a US base salary range of $115,200–$162,000 USD. The annual cadence is inferred because no explicit pay period is provided and the amounts align with annual base pay for a full-time US intermediate backend engineering role. The midpoint is $138,600 per year; the estimated US market range is $115,000–$165,000 annually.

Core skills

Skills and capabilities most closely associated with this opportunity.

Rust Backend Engineering Distributed Systems Graph Data Modeling API Development ClickHouse NATS JetStream Event-Driven Architecture System Design Observability Incident Response Ruby on Rails Data Pipelines Code Review Asynchronous Collaboration

Sample interview questions

How would you design a reliable indexing pipeline that turns SDLC events into a property graph?

I would start by defining stable event schemas, idempotency keys, ordering and replay expectations, and graph entity identifiers. I would use durable messaging, track processing checkpoints, make writes idempotent, and expose metrics for lag, failures, retries, and data freshness. I would also document recovery and backfill procedures so the pipeline can be operated safely.

What trade-offs would you consider when implementing multi-hop graph queries for analytical workloads?

I would consider traversal depth, cardinality growth, filtering opportunities, query timeouts, and tenant isolation. I would push selective filters as early as possible, define sensible limits and pagination, and use query plans or precomputed structures where they improve predictable performance. I would validate the approach against representative production-scale data.

Describe how you would improve operational readiness for a new Rust backend endpoint.

I would add structured logs, request and error metrics, latency instrumentation, dashboards, and alerts tied to user-impacting failure modes. I would write tests for normal, authorization, validation, and failure paths, document expected behavior and rollback steps, and ensure the endpoint has appropriate rate, resource, and access controls.

How do you use AI-assisted development tools while maintaining code quality and correctness?

I use AI tools to accelerate exploration, boilerplate, test ideas, and documentation, but I treat their output as untrusted. I verify generated code against requirements, existing architecture, security expectations, and automated tests, then perform normal code review. I also avoid exposing sensitive data and document important design decisions myself.

How would you communicate a technical risk or ambiguous requirement in an asynchronous team?

I would write a concise issue or design note describing the context, the specific ambiguity or risk, affected systems, assumptions, options, and my recommended path. I would ask targeted questions, set a decision deadline when appropriate, and update the document once a decision is made. This creates alignment across time zones and leaves an auditable record for future maintainers.

Opportunity details

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.

As an Intermediate Backend Engineer on the GitLab Orbit 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 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.
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 (for example, incident response and follow-ups).
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, refine problem statements with teammates, and communicate progress clearly in writing across time zones in an all-remote team.

About the team

GitLab Orbit sits within the Data Engineering organization. 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 rather than following a feature factory model. A key challenge for us right now is scaling sustainably as usage grows and team members take time off. That includes hardening multi-tenant behavior. At the same time, we’re bringing Knowledge Graph to

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

$115,200—$162,000 USD

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