AI role · Open offer
AI Engineer — LangChain platform
LangChain
This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.
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At a glance
How you'll work, where, and key listing dates.
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- 535 Mission Street, San Francisco, CA 94105, United States · Hybrid
- Compensation
- $175k–$240k + equity
- Experience level
- Senior
- Team
- Platform
- Benefits & perks
About this role
What the team is looking for and how this position fits.
LangChain is hiring a Senior AI Engineer — LangChain platform on the Platform team (Hybrid · San Francisco, United States). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.
You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.
LangChain builds the most widely used framework for LLM applications—LangChain, LangGraph, and LangSmith.
AI Engineer (SF): improve developer experience, agent reliability, and enterprise deployment patterns for thousands of production apps.
Hybrid SF Mission Bay — open-source culture with equity.
What you will do
- Ship LangGraph patterns for durable agents, human-in-the-loop, and memory.
- Build reference architectures for RAG, evals, and observability with LangSmith.
- Work with enterprise design partners on VPC and SSO requirements.
- Contribute to OSS docs, cookbooks, and conference talks.
Stack & environment
- Python, LangChain, LangGraph, OpenAI API, vector databases.
- Hybrid San Francisco SOMA.
What we look for
- Production LangChain/LangGraph apps or significant OSS contributions.
- Strong API design and developer empathy.
- Comfort presenting to technical audiences.
Skills we're hiring for
What each capability means for this role—not a keyword checklist.
- Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
- LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
- LangGraph
Tools & stack
Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).
- Vector DBsTool
Embedding storage, hybrid search, and freshness strategies for RAG and semantic retrieval.
- DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.
You're a strong fit if…
Tailored to this listing—use it as a self-check before you apply.
- ✓You can show evidence in your application: LangGraph/LCEL production app or OSS contribution link.
- ✓Several of these show up in recent shipped work—not only on your CV: Python, LangChain, LangGraph, OpenAI API, and Vector DBs.
- ✓You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
- ✓You are comfortable with the hybrid rhythm (Hybrid · San Francisco, United States)—onsite collaboration when it matters, deep work when remote.
- ✓You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
Apply
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What the employer asked for
LangGraph/LCEL production app or OSS contribution link.
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