LangChain

About LangChain

Empowering developers to build LLM applications effortlessly

🏒 TechπŸ‘₯ 11-50

Key Highlights

  • Supports over 50,000 companies including Cohere & OpenAI
  • Headquartered in San Francisco, CA
  • Focus on AI/ML and developer tools
  • Rapid application development from idea to production

LangChain is a leading platform for developing applications powered by large language models (LLMs), supporting over 50,000 companies including notable names like Cohere and OpenAI. Headquartered in San Francisco, LangChain offers tools that streamline the AI engineering lifecycle, enabling develope...

🎁 Benefits

LangChain offers competitive salaries, equity options, flexible remote work policies, and generous PTO to support work-life balance....

🌟 Culture

LangChain fosters a culture of innovation and agility, prioritizing rapid development cycles and a strong focus on AI technologies. The team values co...

LangChain

Backend Engineer β€’ Senior

LangChain β€’ San Francisco - On-Site

Posted 1 month agoπŸ›οΈ On-SiteSeniorBackend EngineerπŸ“ San FranciscoπŸ’° $175,000 - $225,000 / yearly
Apply Now β†’

Overview

LangChain is seeking a Senior Backend Engineer to build backend systems for their observability and evaluation platform. You'll work with Java and Spring to develop APIs that support monitoring and evaluation workflows. This role requires strong backend development experience and familiarity with performance tuning.

Job Description

Who you are

You have 5+ years of backend engineering experience building production systems at scale β€” you've owned services from design through deployment and have navigated the complexities of debugging distributed systems. Your deep expertise in Java and Spring allows you to ship high-throughput APIs that handle millions of requests, and you have strong opinions on service architecture and testing strategies. Familiarity with Docker and container orchestration is a plus, as is experience with observability tools like Prometheus and Elasticsearch. You thrive in collaborative environments and enjoy working closely with cross-functional teams to ensure system reliability and performance.

Desirable

Experience with fullstack or frontend engineering would be extremely valuable, as well as a background in performance tuning and debugging production issues. You understand the importance of monitoring and evaluation in AI applications and are eager to contribute to the development of tools that enhance these capabilities.

What you'll do

In this role, you will design, develop, and maintain backend services and APIs that support LangSmith’s tracing, monitoring, and evaluation workflows. You will collaborate on architectural decisions to ensure that systems are performant and maintainable, optimizing storage and query performance for high-volume observability and evaluation data. Your work will directly impact how developers monitor and evaluate their AI applications at scale, making their systems more reliable and efficient.

You will also be responsible for ensuring system reliability through strong testing and monitoring practices. This includes implementing best practices for code quality and performance, as well as participating in code reviews to maintain high standards across the team. You will work closely with product managers and other engineers to define requirements and deliver features that meet user needs.

What we offer

LangChain offers competitive compensation that includes a base salary ranging from $175,000 to $225,000, meaningful equity, and comprehensive benefits such as health and dental coverage, flexible vacation, a 401(k) plan, and life insurance. We believe in fostering a supportive and inclusive work environment where every team member can thrive. You will have the opportunity to work with cutting-edge technologies and contribute to a mission-driven company that is making intelligent agents ubiquitous. We encourage you to apply even if your experience doesn't match every requirement β€” your unique perspective could be just what we need.

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