GTM Engineer

Stack

Responsibilities

• ▹Design and deploy production-grade customer agents using LangGraph and LangSmith for technical support, integration troubleshooting, and complex onboarding flows
• ▹Drive case deflection by analyzing customer friction points and building self-service AI systems that reduce support volume while improving experience quality
• ▹Own the domain: proactively identify opportunities, propose architectures, and own the full lifecycle of the systems you build
• ▹Dogfood the stack: feed learnings back to the product team and contribute to the LangChain and LangGraph open-source ecosystem
• ▹Build AI-native onboarding workflows that automate documentation retrieval and code generation, helping enterprise customers move from prototype to production faster

Requirements

• ▹Deep understanding of the LLM stack: prompting, retrieval (RAG), cognitive architectures, and agentic loops; likely already built with LangChain or LangGraph
• ▹Strong software engineering background (typically 3+ years), including at least 1 year shipping LLM systems in production
• ▹Self-directed, founder-like mentality: comfortable navigating ambiguity and driving high-impact problems to completion without needing a ticket
• ▹Full-stack capability: strong coding skills in Python or TypeScript (ideally both), able to build end-to-end applications
• ▹Customer-centric: able to translate a customer's technical pain point into a scalable system architecture

Nice to have

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

Soft skills

What we offer

• ▹Salary: $160K-$180K
• ▹Competitive compensation: base salary + meaningful equity + benefits
• ▹Medical, dental, and vision coverage, flexible vacation, 401(k) plan, life insurance
• ▹Meals on in-office days (US)
• ▹Locally competitive benefits for team members in the EU, UK, and APAC

About the company

LangChain's mission is to make intelligent agents ubiquitous. It grew from widely adopted open-source tools into a platform (LangSmith: Observability, Evaluation, Deployment, Fleet, Sandboxes; open-source frameworks LangChain, LangGraph, Deep Agents; and the new LangSmith Engine). It raised $125M at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures. It has 100M+ monthly open-source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 (plus 35% of the Fortune 500) use LangSmith in production, including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.