SmithDB

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, LangChain, LangGraph, LangSmith, and Fleet are used by teams shipping real AI products across startups and large enterprises. Millions of developers trust LangChain to power AI teams at companies like Replit, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, and 35% of the Fortune 500.

We're building a storage layer specifically designed for AI observability and evaluation. We're a fast-moving team looking for a systems / database engineer to help design, optimize, and harden our system. Within 6 months, we've gone from idea to production system but there is still lots to build and optimize. You'll be working on execution, storage layout, performance profiling, and scaling to trillions of traces. This role is based in San Francisco.

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  Engineer performant Rust code for ingestion and query execution

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  Optimize for cost and speed, heavily utilizing memory and CPU profiling

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  Integrate tightly with cloud object stores (S3/GCS/Azure Blob)

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  Deploy our distributed database services on Kubernetes (multi-tenant, high throughput, low latency)

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  Contribute to observability for the storage engine itself (metrics, tracing, debug tooling)

• 5+ years in systems/database engineering with strong experience in a systems programming language (Rust, C++). Rust strongly preferred, as it is the language we use.

• Knowledge of database internals is a plus: query engines, storage systems, indexing, and compaction

• Proficiency in systems performance analysis: memory allocation, CPU hotspots, lock contention, async runtimes.

• Familiarity with similar query execution engines is a plus

• Experience with Kubernetes, distributed systems, and cloud object storage is a plus.

• Strong fundamentals in networking, OS concepts, and systems debugging.