LaunchDarkly
Full Stack Engineer, Observability
About this role
LaunchDarkly is hiring a Backend Engineer to build Vega, an AI-powered observability platform expanding into feature management. You'll architect scalable Go services, data pipelines, and agent infrastructure while collaborating across product and engineering teams to ship enterprise-grade capabilities.
What you'll do
- Design and build backend features including APIs, Go services, agent infrastructure, and data pipelines for the Vega platform
- Own features end-to-end from prototyping through production deployment and iteration
- Operate high-throughput data systems ensuring reliability, performance, and cost efficiency at scale
- Collaborate cross-functionally with product, design, and engineering teams across Observability and Feature Management
- Write well-tested, maintainable code and establish best practices for quality and observability
- Participate in on-call rotations and take ownership of production reliability
What they're looking for
- Go programming (or Java, Rust, C++)
- API and distributed systems design
- LLM integration and AI agent development
- Data-intensive systems and streaming pipelines
- Columnar databases (e.g., ClickHouse)
- High-volume data ingest and processing
- Sandboxing and isolation technologies
- Product thinking and requirements translation
Benefits
- Restricted Stock Units (RSUs)
- Health, vision, and dental insurance
- Mental health benefits
- Remote work (US-based)
- Collaborative, high-trust engineering culture
- Mentorship and professional growth opportunities
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LaunchDarkly
LaunchDarkly builds a platform for feature management, AI configuration, and observability that helps teams control, monitor, and safely deploy software. The company is hiring Full Stack Engineers, SDK developers, and security specialists to expand its core platform capabilities, including AI-powered onboarding, release safety monitoring, enterprise integrations, and developer security tooling.
- Website
- launchdarkly.com
Likely interview questions
- Describe a production LLM-powered feature you built—how did you handle reliability, latency, and cost?
- Walk us through your approach to designing a sandboxed execution environment for untrusted agents, including isolation and resource limits.