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Salary: USD 145,500 - 235,400 / annual
LaunchDarkly is hiring a Backend Engineer to build and expand Vega, an AI-powered platform that surfaces insights and automates actions across LaunchDarkly's products. This is a greenfield opportunity to evolve Vega from an Observability-focused assistant into a cross-product platform serving Feature Management customers and powering a new usage-based SKU.
You'll design, build, and ship backend features across the Vega platform, including APIs, Go services, agent infrastructure, and data pipelines. Responsibilities include owning features end-to-end from prototype through production rollout, operating high-throughput data systems reliably and cost-efficiently at scale, and collaborating cross-functionally with product, design, and engineering teams across Observability and Feature Management.
Key focus areas include expanding Vega beyond Observability into Feature Management (including a flag cleanup agent), designing and operating safe, sandboxed AI agent execution infrastructure with isolation and resource limits, building data import/export pipelines to connect Observability to other platforms, and creating a platform that other teams can contribute to. You'll also work on natural language dashboard creation, metering and usage pipelines for usage-based monetization, and contribute to architecture decisions and engineering standards.
Required: 5+ years of professional software engineering experience shipping production-quality backend systems, proficiency in Go (or Java, Rust, C++), experience designing APIs and distributed services, and experience building or integrating LLM-powered features or AI-driven workflows. Strong plus: data-intensive systems (streaming pipelines, columnar stores like ClickHouse), product instincts, familiarity with feature flagging or observability tooling, and experience with usage-based products or sandboxing technologies.
You'll participate in on-call rotations, mentor teammates, write well-tested maintainable code, and promote best practices for quality, observability, and reliability.
