---
title: 'Full Stack Engineer, Observability at LaunchDarkly'
canonical: 'https://feeny.ai/job/full-stack-engineer-observability-launchdarkly-united-states-4y5xrg3xq7w7'
type: 'job'
last_seen: '2026-09-11'
---

# Full Stack Engineer, Observability at LaunchDarkly

- **Company:** LaunchDarkly
- **Location:** United States
- **Work type:** remote
- **Posted:** 2026-09-03
- **Last confirmed live:** 2026-09-11
- **Apply:** https://job-boards.greenhouse.io/launchdarkly/jobs/7985794003

## Job description

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.

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.

## About LaunchDarkly

## Company Overview
- **One-liner**: LaunchDarkly provides a runtime control layer for AI-era software development, enabling teams to release features safely, experiment in production, and govern AI agents.
- **Entity Type**: Private (venture-backed, Series D+) with investors including Redpoint, Threshold Ventures, and Benchmark.
- **Headquarters**: Oakland, California, USA (with additional offices in London, UK; Bangalore and Mumbai, India)
- **Founded**: 2014
- **Founders**: Edith Harbaugh (CEO/Executive Chair) and John Kodumal (CTO)

## Core Business
- **Primary industry**: Feature management / software tooling / developer infrastructure
- **Target customers**: B2B, serving both large enterprises (25% of Fortune 500) and small startups; teams in engineering, product, marketing, and business operations.
- **Mission or purpose**: “Making software releases boring since 2014” and “creating a world in which every AI-powered release is safe and unceremonious.”

## Products & Services
- **Feature Flags (CodeControl)**: Wrap new or risky code in flags to enable progressive rollouts, automated rollbacks, and instant remediation — without redeploying.
- **Experimentation (A/B/n testing, multi-armed bandits)**: Run online experiments on code and agents to optimize performance based on real-world results.
- **Observability (Guardian Monitoring, Error Monitoring, Logs & Traces, Session Replay)**: Monitor feature behavior and auto-remediate issues.
- **AgentControl**: Govern, observe, and steer AI agents in production — including adaptive triggers, progressive rollouts, agent observability, custom judges, and LLM playground.
- **AI Insights & Prompt Management**: Test prompts/models in production, dynamically route traffic to best options, and manage prompt snippets.

## Market Standing
- **Valuation/Market Cap**: Not publicly available (last reported Series D in 2021 was at a ~$3 billion valuation per industry sources; not confirmed in provided materials)
- **Key Metric**: Total Funding – not disclosed in provided sources. Annual Revenue – not publicly available. Proxy: 5,500+ customers, 45 trillion feature flags evaluated daily.
- **Notable Investors/Partners**: Board members include Scott Raney (Redpoint), Josh Stein (Threshold Ventures), and Jonathan Heiliger (Benchmark); listed on Forbes Cloud 100 for six consecutive years.
- **Growth Signals**: >570 employees and growing; 25% of Fortune 500 are customers; expansion into AI agent control (AgentControl product); remote-first hiring globally.

## Competitive Advantages
- **#1 in Feature Management** for three years running (as claimed on website).
- **Massive scale**: 45 trillion feature flags evaluated daily, providing unmatched performance and reliability.
- **Integrated platform** combining feature flags, experimentation, observability, and AI agent governance in one runtime control layer — reducing tool sprawl.
- **Strong brand trust** among 5,500+ customers, including many Fortune 500 enterprises.

## Strategic Focus
- **Lead the AI transformation**: Apply runtime control principles to AI agents and LLM-powered features (AgentControl, LLM Playground, custom judges).
- **Enable self-healing systems**: Automatically detect and remediate failing code or misbehaving agents without human intervention.
- **Continue expanding experimentation** and observability capabilities to help teams continuously optimize.

## Why Work Here
- **Culture**: “Humble, open, collaborative, respectful and kind” – as stated on the careers page. Equal opportunity and affirmative action employer.
- **Remote/hybrid policy**: Many engineering roles are fully remote (US, India, other locations). Some roles like IT are hybrid in Oakland or Bangalore/Mumbai.
- **Work environment**: High-growth (570+ employees) with significant investment in engineering (multiple engineering teams: Core, Measure, Release, AI, etc.).
- **Notable perks**: Pay transparency policy (disclosure of compensation upon request), commitment to diversity, and formal accommodation process. The company is a federal contractor, so it adheres to strict non-discrimination and pay disclosure rules.
- **Engineering focus**: Opportunity to work on cutting-edge problems in feature management, experimentation, and AI governance at massive scale.

## Sources
1. [LaunchDarkly Website](https://launchdarkly.com/) – Main product description, company overview, stats
2. [LaunchDarkly About Us](https://launchdarkly.com/about-us/) – Founding story, leadership team, investors, office locations
3. [LaunchDarkly Careers](https://launchdarkly.com/careers/) – Culture, equal opportunity statement, pay transparency policy
4. [Greenhouse Job Board](https://job-boards.greenhouse.io/launchdarkly/jobs/7738584003) – Open positions, remote/hybrid details, team structure
5. [LinkedIn Company Page](https://www.linkedin.com/company/launchdarkly) – Company description, employee count

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