GTM Engineer

LangChain•New York, NY

About The Position

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, LangChain, LangGraph, LangSmith, and Fleet are used by teams shipping real AI products across startups and large enterprises. Millions of developers trust LangChain to power AI teams at companies like Replit, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, and 35% of the Fortune 500. About the Role: We are looking for an GTM Engineer where you won’t just be using our tools—you’ll be the "First Customer," building the AI-native systems that power our technical support, onboarding, and customer success engines. You will own the technical roadmap for how LangChain supports its users. Your goal is to drive massive case deflection and a premium onboarding experience by building autonomous agents that solve complex technical problems before a human ever needs to step in. This is a role for a builder-operator who can identify a friction point in the customer journey and ship a production-grade agentic solution to fix it.

Requirements

AI-Native Developer: You have a deep understanding of the LLM stack: prompting, retrieval (RAG), cognitive architectures, and agentic loops. You have likely already built with LangChain or LangGraph.
Engineering Foundation: You are a strong software engineer (typically 3+ years) with at least 1 year of experience specifically shipping LLM systems in production.
Self-Directed "Founder" Mentality: You don't need a ticket to tell you what to fix. You are comfortable navigating ambiguity, identifying high-impact problems, and driving them to completion autonomously.
Full-Stack Capability: Strong coding skills in Python or TypeScript (ideally both) and the ability to build end-to-end applications.
Customer-Centric: You enjoy the intersection of high-level engineering and direct customer impact. You can translate a customer's technical pain point into a scalable system architecture.

Nice To Haves

Expertise with LangSmith for evaluation and monitoring.
Experience building or maintaining open-source projects.
A background in technical consulting, solutions engineering, or high-growth GTM teams.

Responsibilities

Architect Customer Agents: Design and deploy production-grade agents (using LangGraph and LangSmith) that handle technical support queries, troubleshoot integrations, and guide users through complex onboarding flows.
Drive Case Deflection: Analyze customer friction points and build self-service AI systems that significantly reduce support volume while improving the quality of the customer experience.
Own the Domain: Act as the product owner and the technical muscle proactively identifying opportunities for improvement, propose architectures, and own the full lifecycle of the systems you build.
Dogfood the Stack: Be a key member of the feedback loop for our product team. As you build complex systems for our customers, you’ll identify gaps in our frameworks and contribute back to the LangChain and LangGraph open-source ecosystem.
Build Onboarding Workflows: Develop "AI-native onboarding" experiences that help enterprise customers move from prototypes to production faster by automating documentation retrieval and code generation.