LangChain
GTM Engineer
About this role
LangChain seeks a GTM Engineer to build production-grade AI agents that streamline customer support, onboarding, and success. You'll act as the first customer, architecting autonomous systems using LangGraph and LangSmith to reduce support friction and improve user experiences while providing feedback to shape the core product.
What you'll do
- Design and deploy production agents using LangGraph and LangSmith for technical support and onboarding
- Identify customer friction points and build self-service AI systems to drive case deflection
- Own the full lifecycle of technical systems from conception through production deployment
- Provide feedback to the product team as you dogfood LangChain and LangGraph in real-world scenarios
- Develop AI-native onboarding workflows to help enterprise customers reach production faster
- Act as product owner, proactively identifying improvement opportunities and proposing architectures
What they're looking for
- LLM stack expertise (prompting, RAG, cognitive architectures, agentic loops)
- Python or TypeScript (ideally both)
- Production LLM systems experience
- Full-stack application development
- LangChain and LangGraph proficiency
- Software engineering fundamentals (3+ years)
- Self-directed problem-solving and autonomous execution
- Customer-centric technical translation
Benefits
- Competitive base salary ($160K-$180K)
- Meaningful equity
- Medical, dental, and vision coverage
- Flexible vacation
- 401(k) plan
- Life insurance
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LangChain
LangChain builds platforms and frameworks for developing, deploying, and observing production AI agents at enterprise scale, including LangSmith for AI observability and evaluation. The company is hiring Deployed Engineers to work directly with enterprise customers on agent implementation and operations, as well as Fullstack Engineers to build features across its platform stack.
- Website
- langchain.com
Likely interview questions
- Walk us through a production LLM system you've built—what was the architecture, and what were the key challenges you solved?
- How would you approach identifying and quantifying a customer friction point in our onboarding or support flow, and what metrics would you track?