Developer Productivity

About Us

At Lang Chain, 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. Lang Chain is a place where your contributions can shape how this technology shows up in the real world.

Today, our platform includes Lang Smith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (Lang Chain, Lang Graph, and Deep Agents), and the newly launched Lang Smith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active Lang Smith customers, and 5 of the Fortune 10 use Lang Smith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, Linked In, , Nvidia, and Bridgewater.

The Infrastructure team builds and maintains the systems that power Lang Chain’s developer platform, including Lang Graph Cloud and Lang Smith. 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 Lang Graph Cloud/Platform and Lang Smith 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.

Location: In person 5 days/week in San Francisco, CA or New York, NY

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

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

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

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

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

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

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

• A pull-request ephemerous end-to-end testing harness that deploys a minimal Lang Smith 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

• 3+ years of experience as a software engineer or infrastructure engineer

• Strong hands-on experience with Python and testing frameworks such as pytest

• Experience working with CI/CD systems (Git Hub Actions preferred) and improving pipeline…