$ cat jobs/research-engineer-langsmith-engine-langchain-0a6d066a9f00.json
Research Engineer, LangSmith Engine
ABOUT US At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater. ABOUT THE TEAM: The LangSmith Engine team is building a proactive agent engineer that analyzes production traces, identifies important failures, recommends and writes fixes, and helps prevent those issues from coming back. We’re building agents that can understand complex software systems and continuously improve the quality of other AI agents. ABOUT THE ROLE: We’re looking for an experienced research engineer to help make the Engine agent more capable and more efficient. You’ll study real agent failures, build benchmarks that capture what matters, run experiments to improve performance, and turn successful ideas into production. This may include prompting and agent-harness i
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