Level

LangChain

Software Engineer, Agent Systems (GTM Engineering)

LangChain is hiring a Software Engineer, Agent Systems (GTM Engineering) in San Francisco, United States. It pays $165k-$220k a year and Level rates it ; you can apply on Level.

AI in this role

openaianthropiclanggraphclay
ai-agents
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:

GTM Engineering builds the AI agents, systems, and automation that power how our go-to-market teams work. We partner across Sales, Marketing, Customer Success, Support, and other GTM functions to identify high-leverage problems and build solutions that improve speed, quality, and scale. Our work spans four core areas:

  • Identify — find high-leverage GTM workflows where AI can meaningfully improve how we operate

  • Build — design, build, and deploy production AI agents and automated workflows across GTM

  • Enable — drive adoption through thoughtful rollouts, playbooks, best practices, and ongoing enablement

  • Evangelize — share what we build and learn externally through content, demos, talks, and open source examples

About The Role:

You'll own the health, cost, performance, and business impact of the GTM Agent System, and build the feedback loops that keep it improving. Because we build the platform we run on, you'll also operate the agent on LangSmith the way we tell customers to, and turn that practice into the reference story enterprises keep asking us for. You'll work across Python 3.11, FastAPI, LangGraph, DeepAgents, LangSmith, Supabase Postgres, BigQuery, Anthropic and OpenAI models, and Slack and Next.js surfaces.

What You'll Do:

  • Monitor production health across every graph, catching errors, slow runs, expensive runs, and silent failures before reps report them

  • Triage incoming issues from Slack, tickets, and rep reports, fixing small things directly and routing the rest to the right owner

  • Run the weekly eval suite, investigate failures, and turn real production bugs into permanent regression tests

  • Track cost and latency by model, graph, use case, and role, and recommend concrete changes to model choice, reasoning effort, and caching

  • Track usage and adoption per rep and per feature, and own the weekly health report the team runs on

  • Build the business metrics that show leadership what the agent is worth, from reply rates and meetings booked to hours reclaimed and ROI

  • Build our own monitoring and alerting on LangSmith, and write the “how we run our own agent” story for customers

What You'll Bring:

  • Strong production Python and SQL, comfortable working in traces, logs, and warehouse tables

  • Real experience running LLM applications, including tracing, evals, and prompt and cache mechanics

  • SRE or production operations instincts: percentiles, SLOs, and separating noise from real pattern

  • Healthy skepticism about metrics; you check what a number actually counts before you publish it

  • Clear writing skills, and interest in publishing what you learn

  • High agency; you notice what's missing and take initiative to build it

Nice to Haves:

  • LangGraph or LangSmith experience

  • Experience building an eval suite from scratch

  • BigQuery or dbt

  • Prior DevRel-adjacent writing

  • Empathy for sales and go-to-market users

Salary: $165,000 - $220,000

Compensation Philosophy:

We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks.

We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. 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.

How we rate this

Software Engineer, Agent Systems (GTM Engineering) at LangChain rates 68 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

AI agentsOpenAIAnthropicLangGraphClay

Questions you could be asked

  1. How do you decide when an AI agent can act on its own versus asking for approval first?
  2. Walk me through how you've used OpenAI in your day-to-day work.
  3. What are the limits of Anthropic that you've run into, and how did you work around them?
  4. What's a project where you used LangGraph hands-on?
  5. Walk me through how you've used Clay in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: AI agents, OpenAI, Anthropic, LangGraph, and Clay. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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