Launchdarkly · Remote - US

Full Stack Engineer, AgentControl

🏢 Launchdarkly📍 Remote - US🕐 Posted 13 days ago
⏱ Full-time🌐 RemoteTechnology✅ Direct from employer ATS
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About this role

About the Job: As a Full Stack Engineer on LaunchDarkly’s AgentControl team, you'll build critical systems such as the web application that customers interact with on a daily basis and distributed systems that power AI evaluations at scale. These systems enable customers to control, monitor, and optimize their agent’s functionality. You’ll primarily work in Go, Python, and Typescript and will work directly with other technologies and tools such as AWS, CockroachDB, and Datadog

LaunchDarkly’s AgentControl team is on a mission to manage the complete software development lifecycle for shipping agents to production, from configuring to benchmarking to observing and beyond. This team has a huge opportunity ahead of it; we're growing fast and we need your help writing the next chapter in our story

Responsibilities: Build and maintain scalable backend services and APIs that power the AI Configs product Collaborate with internal stakeholders, including product managers, designers, and other LaunchDarkly engineering teams, to understand real-world challenges and improve GenAI workflows using our own platform

Continuously optimize the performance, reliability, and scalability of our systems Participate in architecture and design discussions, offering thoughtful input and collaborating with senior engineers to identify tradeoffs and make well-informed decisions Keep up-to-date with the latest developments in the AI field and in software development best practices Take ownership of code in production, participate in on-call rotations, and help improve the reliability and maintainability of our systems over time

Qualifications: 5+ years of professional software engineering experience, with a track record of shipping production-quality full-stack features

Hands-on experience building GenAI features, Agents, and/or working with Large Language Models (LLMs), either through direct model int

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