AI role · Open offer

AI Engineer — LangChain platform

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LangChain

Full-time
Senior
Platform
Hybrid
535 Mission Street, San Francisco, CA 94105, United States · Hybrid
San Francisco, United States
$175k–$240k + equity

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.
OpenAI API

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, OpenAI API, LangGraph, 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

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

LangGraph/LCEL production app or OSS contribution link.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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