Developer Productivity

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 Infrastructure team builds and maintains the systems that power LangChain’s developer platform, including LangGraph Cloud and LangSmith. The team focuses on reliability, scalability, and developer productivity across the stack, working closely with backend, frontend, and platform engineers to ensure services are deployed, tested, and operated with confidence.

We’re hiring a Software Engineer to join the Infrastructure team and own developer productivity across our LangGraph Cloud/Platform and LangSmith products. You’ll work closely with Infrastructure, Backend, and Frontend teams to ship with confidence across Kubernetes-based services, APIs, and UI flows. You’ll also help pioneer quality practices specific to LLM applications, such as prompt regression testing and evaluation suites.

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  Own test strategy end-to-end across APIs, services, UI, data, and infrastructure (Kubernetes, Terraform, Helm)

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  Stand up ephemeral test environments in Kubernetes for pull requests and release candidates; seed test data and run hermetic test suites

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  Shift quality earlier in CI/CD pipelines (GitHub Actions) through parallelization, caching, deterministic seeds, flake tracking, and quality gates

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  Build observability into testing workflows with rich failure artifacts such as logs, traces, and dashboards

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  Establish performance and reliability baselines for critical paths, including SLIs, SLOs, and regression detection

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  Partner on incident workflows by reproducing issues, adding targeted regression tests, and improving runbooks and postmortems

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  Write documentation including test plans, playbooks, and contributor guidelines for writing high-quality tests

• A pull-request ephemeral end-to-end testing harness that deploys a minimal LangSmith stack in CI and runs Playwright and API suites against seeded tenants

• A k6 performance scenario that simulates multi-tenant traffic and surfaces p95/p99 latency regressions per release

• A flake-budget system that automatically quarantines flaky tests, opens issues with artifacts, and tracks time-to-deflake

• Experience with load and performance testing tools such as k6

• Familiarity with observability tooling such as Datadog or OpenTelemetry

• Experience testing services running on Kubernetes and containerized environments

• Basic infrastructure experience with Helm, Terraform, Kubernetes networking, or secrets management

• SQL fluency for validating data (Postgres, ClickHouse, BigQuery)

• Familiarity with Go, Node, or React for targeted white-box tests and improving system testability