EngineeringOn-site

GTM Engineer

LangChain · On-site

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Last seen by MeritLog September 9, 2026Source: AshbySource version: ashby-public-job-posting-v1

MeritLog read this listing from LangChain's Ashby job board and last checked it on September 9, 2026.

Source: the employer's Ashby job board. Open the original listing for current details.

Job details

Work model: On-site
Salary: $160,000 – $180,000
Location: San Francisco, CA
Company website: docs.langchain.com

Hiring context

How this role compares at LangChain

LangChain has 106 live roles in MeritLog’s catalog across 7 job families, and 48 of them are in engineering. 63 of those listings publish a pay range, a disclosure rate of 59%.

LangChain concentrates this hiring in:

Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.

What the role asks for

What you'd do

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.
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.

What they're asking for

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

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job description

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 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. YOU WILL: - 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. WHAT WE ARE LOOKING FOR: - 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. Compensation: - We offer competitive compensation that includes base salary, meaningful equity, benefits, and perks. Benefits include things like medical, dental, and vision coverage, flexible vacation, a 401(k) plan, and life insurance. - Salary: $160K - $180K Compensation Philosophy: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. Actual compensation and offerings will vary based on role, level, and location. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

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