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.
The Enablement team helps customers build real fluency with agent engineering and the LangSmith platform through live training, hands-on workshops, and technical content that scales beyond 1:1 time.
You'll set the technical foundation for how customers learn to build reliable agents with the LangChain ecosystem, teaching their teams to work effectively with LangChain, LangGraph, Deep Agents, and LangSmith through instructor-led workshops, written content, and reference implementations. We work closely with the broader GTM org to make sure every customer has the skills and confidence to build independently.
You are someone who's built real agent systems, can defend the tradeoffs in them, and genuinely loves teaching, whether that's a live workshop for 50 engineers or a debugging session with one stuck developer. You'll also build the internal agents and tools that make the Enablement team itself more efficient.
Design and deliver live, hands-on workshops that build real product fluency, not just familiarity
Create enablement assets (tutorials, reference implementations, best-practice guides) that scale beyond individual sessions
Offer technical guidance or office hours as questions come up
Build internal agents and tools that streamline how the Enablement team operates, automating processes so the team scales efficiently
Act as the voice of the customer inside LangChain, feeding friction points back to Product and Engineering
Stay current on agent engineering practices and fold what you learn into what you teach
Technical:
3+ years building LLM/agent applications, with experience designing agent architectures and evaluation strategies
Strong Python, comfortable writing and debugging code live, in front of a customer
Customer-facing & Education:
2+ years in a technical, customer-facing role (Enablement, Customer Success Engineering, Solutions Engineering, or similar), including experience designing and delivering live workshops
A genuine excitement for teaching, the kind where you'd rather leave a customer more capable than impressed
Demonstrated ability to create and deliver high-quality technical training programs, including live workshops, written tutorials, documentation, and video guides
Exceptional presentation and communication skills, with the ability to explain complex technical concepts to diverse audiences, from individual developers to enterprise stakeholders
Additional:
Comfortable operating independently in ambiguity and managing several customer engagements at once
Curiosity to stay at the forefront of agent engineering in industry to identify evolving trends and quickly incorporate learnings into customer enablement materials
Willing to travel up to 20% of the time
You've deployed AI agents in production, especially using LangChain, LangGraph, Deep Agents, or similar frameworks
Hands-on experience with LLM evaluation, observability, or guardrails
Experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts
TypeScript/JavaScript in addition to Python
Compensation:
$150-$195k + equity
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 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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