---
title: 'Agent Reliability Engineer, GTM at LangChain'
canonical: 'https://feeny.ai/job/agent-reliability-engineer-gtm-langchain-san-francisco-q8bs83cj5vh4'
type: 'job'
last_seen: '2026-09-10'
---

# Agent Reliability Engineer, GTM at LangChain

- **Company:** [LangChain](https://feeny.ai/companies/langchain)
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-09-06
- **Last confirmed live:** 2026-09-10
- **Apply:** https://jobs.ashbyhq.com/langchain/eadd2a71-47fc-483b-948f-4b2384f7f93f

## 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, 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, 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: $150,000 - $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.

## About LangChain

## Company Overview
- **One-liner**: LangChain provides an agent engineering platform and open source frameworks that help developers build, evaluate, deploy, and operate reliable AI agents at scale.
- **Entity Type**: Private (Series B, unicorn)
- **Headquarters**: San Francisco, California, United States (with offices in New York, Boston, and Amsterdam)
- **Founded**: Early 2023
- **Founders**: Harrison Chase and Ankush Gola

## Core Business
- **Primary industry/industries**: Artificial Intelligence, Developer Tools, Agent Engineering
- **Target customers**: B2B – developers and engineering teams at enterprises (35% of Fortune 500 are customers) and startups
- **Mission or purpose statement**: “Make intelligent agents ubiquitous” – develop tools that make it easy to build reliable agents that can use data and take actions.

## Products & Services
- **[LangSmith](https://www.langchain.com/)**: Commercial agent engineering platform offering observability, evaluation (LLM-as-judge, human feedback), and deployment. Includes LangSmith Engine for automated issue detection and root cause analysis. Supports Python, TypeScript, Go, and Java SDKs.
- **[LangGraph](https://www.langchain.com/)**: Open source framework for building production agents with low-level control, durable checkpointing, and support for human-in-the-loop, multi-agent swarms, and A2A/MCP protocols.
- **[LangChain](https://www.langchain.com/)**: Original open source framework for building agents quickly with templates and any model provider.
- **[Deep Agents](https://www.langchain.com/)**: Agent harness for autonomous, long-horizon tasks with built-in memory and concurrency.
- **[Fleet](https://www.langchain.com/)**: No-code agent builder that lets non-technical users describe tasks in plain language; agents act across daily tools with enterprise security.

## Market Standing
- **Valuation/Market Cap**: $1.25 billion (unicorn status, per November 2025 Series B round)
- **Key Metric**: Total Funding – $160 million (Seed: $10M led by Benchmark; Series A: $25M led by Sequoia Capital; Series B: $125M led by IVP). Annual Revenue (LinkedIn estimate): $8.5 million.
- **Notable Investors/Partners**: IVP (lead Series B), Benchmark (lead Seed), Sequoia Capital (lead Series A). Customers include Zip, Vanta, Klarna, Workday, LinkedIn, Cloudflare.
- **Growth Signals**:
  - Headcount: 212 employees (+264.8% YoY, +9.9% monthly)
  - 1 billion+ monthly open source downloads
  - 1 billion+ events ingested per day on LangSmith
  - 35% of Fortune 10 are LangSmith customers
  - 6,000+ active LangSmith customers
  - Awards: Forbes AI 50, InfraRed 100, IA40, Enterprise Tech 30
  - Active job postings: 123 (monthly +4.2%, yearly +668.8%)

## Competitive Advantages
- **Open source ecosystem**: LangChain and LangGraph are the most popular open source frameworks for building LLM agents, with a massive community (100M+ monthly downloads) that drives adoption and talent.
- **Full lifecycle platform**: Covers building (open source frameworks), evaluating (LangSmith), deploying (agent server), and operating (LangSmith Engine) – a vertically integrated solution.
- **Framework-agnostic**: LangSmith works with any agent stack (OpenAI, Anthropic, etc.) via SDKs, reducing vendor lock-in.
- **Enterprise trust**: Already embedded in Fortune 500 companies; strong security and compliance features for production AI.

## Strategic Focus
- **Agent reliability**: Investing heavily in evaluation and observability to make agents “reliably good” – the hardest problem in production AI.
- **Long-horizon autonomy**: Deep Agents and Fleet target autonomous, multi-step tasks that require memory and human collaboration.
- **Global expansion**: Offices in US, Europe, and India; hiring across sales, engineering, and customer success to serve a growing international customer base.

## Why Work Here
- **Culture**: Fast-moving, open source–first startup with a mission to shape how the world uses AI. Described as hiring “the best in the business” with a focus on engineering excellence.
- **Work policy**: Hybrid/office presence in San Francisco, New York, Boston, and Amsterdam. Remote roles may exist (not explicitly stated, but global hiring suggests flexibility).
- **Notable perks/engineering culture**:
  - Work on cutting-edge agent technology used by millions of developers.
  - High autonomy and impact – small team with big ambitions.
  - Strong alumni network from top tech companies (AWS, Datadog, HashiCorp, GitLab, etc.).
  - Recent departures include VP of Marketing (Diana Smith, Mar 2026) – indicates some churn, but overall rapid hiring.
  - Active job postings across engineering, sales, product, and consulting – strong growth trajectory.

## Sources
1. [langchain.com](https://www.langchain.com/) – Official website (products, stats, customers)
2. [langchain.com/careers](https://www.langchain.com/careers) – Careers page (awards, values)
3. [langchain.com/about](https://www.langchain.com/about) – About page (mission, history, funding, team)
4. [linkedin.com/company/langchain](https://www.linkedin.com/company/langchain) – LinkedIn profile (headcount, revenue, funding rounds, job trends, talent sources)

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