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 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.

Requirements

Must-haves

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

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigopythonpostgresnext.jsrestsqlllm

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)