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# Backend Engineer, Observability

Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.

[Apply for this job](#job-application)[View company](https://jobicy.com/company/launchdarkly.md)Share23 Sep 2026Published26Listing views3Application actions23 Oct 2026Apply before  Opportunity details

## About this role.

AI SummaryLaunchDarkly is hiring a senior-level Backend Engineer to expand Vega, an AI-powered platform for observability and feature management. The role centers on designing Go-based APIs, distributed services, agent infrastructure, high-throughput data pipelines, and usage-metering capabilities. The engineer will own features from prototype through production, contribute to architecture decisions, mentor teammates, and participate in on-call reliability work. Key technical themes include safe sandboxed AI-agent execution, LLM-powered workflows, enterprise data integrations, and scalable observability systems.

## Role DNA

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

### Job Complexity

5/5EasyHard

### Pace & Pressure

4/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

4/5IndependentCollaborative

AI insightThis is a technically demanding greenfield platform role requiring at least five years of backend engineering experience, distributed-systems expertise, and familiarity with AI-agent or LLM integrations. Engineers are expected to make architecture trade-offs, operate production systems at scale, and independently deliver end-to-end capabilities.

## Salary analysis

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

Estimated job medianMarket rate$190,450US market range$150k–$240k0$264k

AI insightThe disclosed yearly base-pay range across all US geographic zones is $145,500 to $235,400, producing an overall offer midpoint of $190,450. A typical US market range for a senior backend engineer building distributed, AI-enabled infrastructure is approximately $150,000 to $240,000 annually; actual compensation varies by location, scope, and equity structure. RSUs and benefits are explicitly stated to be additional to salary and are not included in the salary figures.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[Go](https://jobicy.com/jobs?search_keywords=Go.md)[Backend Engineering](https://jobicy.com/jobs?search_keywords=Backend%20Engineering.md)[Distributed Systems](https://jobicy.com/jobs?search_keywords=Distributed%20Systems.md)[API Design](https://jobicy.com/jobs?search_keywords=API%20Design.md)[LLM Integration](https://jobicy.com/jobs?search_keywords=LLM%20Integration.md)[AI Agents](https://jobicy.com/jobs?search_keywords=AI%20Agents.md)[Data Pipelines](https://jobicy.com/jobs?search_keywords=Data%20Pipelines.md)[Observability](https://jobicy.com/jobs?search_keywords=Observability.md)[ClickHouse](https://jobicy.com/jobs?search_keywords=ClickHouse.md)[Production Reliability](https://jobicy.com/jobs?search_keywords=Production%20Reliability.md)

Sample interview questionsDescribe a backend service you took from an early design through production operation.I would explain the customer problem, API and data-model decisions, reliability requirements, rollout plan, observability instrumentation, and post-launch improvements. I would emphasize measurable outcomes such as latency, availability, adoption, or operating-cost improvements.

How would you design a safe sandbox for AI agents that can execute actions on behalf of users?

I would use isolated execution environments with least-privilege identity, explicit tool permissions, resource and time limits, network controls, immutable audit logs, and approval gates for consequential actions. I would also make execution reproducible and provide clear rollback or recovery paths.

What considerations matter when operating a high-volume event ingestion and analytics pipeline?

I would focus on schema evolution, partitioning, backpressure, idempotency, ordering requirements, retention, data quality checks, cost controls, and end-to-end monitoring. I would define service-level objectives for ingest latency, completeness, and query freshness before selecting technologies.

How would you approach adding natural-language dashboard creation to an observability product?

I would constrain the model through a structured intermediate representation for queries and dashboard definitions, validate generated requests against permissions and schemas, and provide previews before publishing. Evaluation would include correctness, safety, latency, user acceptance, and feedback-driven iteration.

How do you make sound technical decisions when product requirements are ambiguous?

I clarify the user outcome, constraints, risks, and success metrics, then write down a small set of viable options with explicit trade-offs. I align stakeholders on an incremental approach, ship a measurable first version, and revise the design using production evidence.

### About the Job:

LaunchDarkly is looking for a Backend Engineer to help build and expand Vega, our AI-powered platform that surfaces insights and automates actions across LaunchDarkly products. This is a greenfield opportunity: you’ll help evolve Vega from an Observability-focused assistant into a cross-product platform that serves Feature Management customers and powers a new, usage-based SKU.

You’ll build the APIs, services, and agent infrastructure that make Vega and the whole Observability platform broadly valuable—enabling capabilities like AI-driven dashboard creation, flag cleanup automation, and safe, sandboxed agent execution. You’ll work primarily in Go services and data pipelines at significant scale, and collaborate closely with product, design, and other engineering teams to shape how AI capabilities land across LaunchDarkly. Additionally, you’ll work on other Enterprise-grade capabilities for the Observability platform to help drive customer activation and success.

### Responsibilities:

* Design, build, and ship backend features across the Vega platform (APIs, Go services, agent infrastructure, and data pipelines)
* Own features end-to-end: from prototype and API design through production rollout and iteration
* Operate high-throughput data systems and keep them reliable, performant, and cost-efficient at scale
* Collaborate cross-functionally with product managers, designers, and engineers across Observability and Feature Management
* Participate in architecture discussions and technical design reviews, contributing to sound trade-off decisions
* Write well-tested, maintainable code and promote best practices for quality, observability, and reliability
* Mentor teammates and contribute to a collaborative, high-trust engineering culture
* Participate in on-call rotations and take ownership of production reliability for your features

What You’ll Work On

* Expand Vega beyond Observability into Feature Management, including the flag cleanup agent as a cross-product capability
* Design and operate safe, sandboxed AI agent execution infrastructure—isolation, resource limits, permissions, and auditability
* Support enterprise capabilities for the Observability platform: building data import and export pipelines to connect O11y to other platforms and enabling new customer workflows
* Build a platform that other teams can contribute to—defining contribution patterns, shared services, and APIs for agent capabilities
* Power dashboard creation via natural language prompts, extending existing Vega infrastructure with new tools, context, and evaluation
* Build the metering and usage pipelines that enable a usage-based monetization model for Vega
* Contribute to architecture decisions, code reviews, and engineering standards across the Observability and Vega teams

### About You:

* 5+ years of professional software engineering experience, with a track record of shipping production-quality backend systems
* Proficient in Go (or a similar language like Java, Rust, or C++) and experienced designing APIs and distributed services
* Experience building or integrating LLM-powered features, agents, or AI-driven workflows
* Experience with data-intensive systems—streaming pipelines, columnar stores (e.g., ClickHouse), or high-volume ingest is a strong plus
* Strong product instincts: you care about how your systems are used and can translate ambiguous requirements into clean, well-scoped solutions
* Collaborative and communicative—you work well across teams and functions, and you’re comfortable navigating ambiguity
* Familiarity with LaunchDarkly, feature flagging, or observability tooling is a plus
* Experience with usage-based product surfaces, sandboxing/isolation technologies, or developer tooling is a plus

Pay:

Target pay ranges based on Geographic Zones* for Level 3:

* Zone 1: San Francisco/Bay Area or NYC Metropolitan Area, Boston, Seattle – $171,200 – $235,400**
* Zone 2: Irvine, LA, Monterey, Santa Barbara, Santa Rosa, Austin, Portland, Philadelphia, Chicago – $154,100 – $211,860**
* Zone 3: All other US locations – $145,500 – 200,090 **

LaunchDarkly operates from a place of high trust and transparency; we are happy to state the pay range for our open roles to best align with your needs. Exact compensation may vary based on skills, experience, and location.

*Within the United States, our geographic pay zones are defined by counties surrounding major metropolitan areas.
**Restricted Stock Units (RSUs), health, vision, and dental insurance, and mental health benefits in addition to salary.

### About LaunchDarkly:

Modern software delivery was supposed to be the foundation for a thriving digital business but reality has proven otherwise. Slow, inefficient development cycles, costly outages, and fragmented customer experiences are preventing developers from building their best software. The LaunchDarkly platform helps developers innovate on new features faster while protecting them with a safety valve to instantly rewind when things go wrong. Developers can target product experiences to any customer segment and maximize the business impact of every feature. And by gradually rolling out new application components, they escape nightmare “big-bang” technology migrations.

The LaunchDarkly platform was built to guide engineers to the next frontier of DevOps by:

* Improving the velocity and stability of software releases, without the fear of end customer outages
* Delivering targeted experiences by easily personalizing features to customer cohorts
* Maximizing the business impact of every feature through the ability to experiment and optimize
* Coordinating the release and optimization of software to provide consistent experiences across mobile platforms and device types
* Improving the effectiveness and productivity of engineering teams, by providing insights into engineering cadence and stability

At LaunchDarkly, we believe in the power of teams. We’re building a team that is humble, open, collaborative, respectful and kind. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, veteran status, or disability status. LD invites any applicant to review our written Affirmative Action Plan. To do so, contact People Ops at [hr@launchdarkly.com](mailto:hr@launchdarkly.com).

Do you need a disability accommodation?

Fill out this [accommodations request form](https://docs.google.com/forms/d/e/1FAIpQLSdYb_7upYMtdRVXzvXGHGfQw0pU2FNma-6Rwp-I6NjKm7SYNw/viewform) and someone from our People Operations team will contact you for assistance.

Your safety matters to us. To protect yourself from potential scams, LaunchDarkly recruiters will only contact you from @[launchdarkly.com](http://launchdarkly.com) email addresses or via LinkedIn from “Verified Recruiter” accounts. Be cautious of emails from other domains. Legitimate LaunchDarkly recruiters will never ask for money, fees, or banking information before making a job offer. LaunchDarkly will never make a job offer without conducting a formal interview process. Our interview process does not involve asking detailed questions by email. If you are ever unsure about a communication that you receive, don’t click any links—visit [Careers | LaunchDarkly](https://launchdarkly.com/careers/) directly for confirmed job openings and links to apply.

Please notify us of any fraudulent representation by sending an email to [careers@launchdarkly.com](mailto:careers@launchdarkly.com).

Show more

[Apply now >](https://jobicy.com/jobs/153974-backend-engineer-observability.md)

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