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LangChain

Fullstack Software Engineer, Applied AI

Reposted 9 Days Ago
In-Office
New York, NY, USA
Mid level
In-Office
New York, NY, USA
Mid level
Implement AI powered workflows and applications, design novel AI architectures, and build evaluation pipelines for internal and customer-facing products.
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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 Team

The Applied AI team builds the agents that show the world what's possible with LangChain. We ship open source reference agents like Open SWE, Open Canvas, and our Deep Research agent that developers across the community use as starting points for their own production systems, while also building internal agents that power LangChain's own GTM and engineering workflows. It's a small, fast-moving team that operates at the frontier, iterating rapidly, running rigorous evals on our own work, and feeding hard-won learnings back into the platform. If you want to work on the frontier of agent-building, this may be the team for you.

About The Role

We’re hiring fullstack Applied AI Engineers to help us build AI agents that power every part of LangChain from Marketing and GTM to Recruiting, Support, Internal Tools, and our Core Product.

In this role you will own a problem space and work closely with that function to design, build, and deploy production-grade agents, workflows, and applications that transform how we operate. Your work will directly accelerate LangChain’s mission to make intelligent, autonomous software a reality both internally and for our customers. Some of these projects will be open source, contributing to the LangChain and LangGraph ecosystem and setting new standards for how companies build with AI.

*This role will be based in our San Francisco or New York office. Employees within commuting distance work from the office are five days per week. Candidates who live outside commuting distance (e.g. >1hr each way), may be eligible for hybrid arrangements depending on location and role requirements.

What You Will Do
  • Design, implement, and deploy end-to-end AI workflows and agents that solve real problems across multiple business domains.

  • Develop and iterate on agent architectures, evaluation pipelines, and performance frameworks to ensure reliability and measurable outcomes.

  • Translate emerging AI research and tooling into practical, production-ready solutions.

  • Communicate technical decisions, trade-offs, and insights clearly to both technical and non-technical stakeholders.

  • Collaborate cross-functionally embedding with teams like Marketing, GTM, Recruiting, or Product to identify opportunities for agent-driven automation and measurable business impact.

  • Contribute to the LangChain and LangGraph ecosystem, including open source components, documentation, and shared tools.

What You Will Bring
  • Experienced software engineer with a strong track record shipping AI or ML-powered applications (typically 3+ years, including at least 1 year building LLM systems in production).

  • Hands-on experience implementing evaluation and monitoring systems for agents or workflows.

  • Deep understanding of the components that make up an AI system: prompting, retrieval, orchestration, inference APIs, and model selection across modalities.

  • Strong coding skills in Python or TypeScript (ideally both).

  • Excellent communicator who can simplify complex technical ideas for diverse audiences.

  • Thrives in a fast-moving, ambiguous startup environment; enjoys identifying the highest-impact problems and driving them to completion.

  • Naturally curious and motivated to learn new tools, frameworks, and approaches in applied AI.

Nice To Haves
  • Expertise with LangChain or LangGraph.

  • Experience building or maintaining open source projects.

  • Background in applied AI research or agentic workflow development.

  • Based in San Francisco (preferred), NYC, or Boston.

Compensation

We offer competitive compensation that includes base salary, meaningful equity, and benefits such as health and dental coverage, flexible vacation, a 401(k) plan, and life insurance. Actual compensation will vary based on role, level, and location. For team members in the EU and UK, we provide locally competitive benefits aligned with regional norms and regulations. Annual Annual Salary Range: $165,000 - $190,000

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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