Technical Support Engineer

Reposted 17 Days Ago
Be an Early Applicant
Berlin
In-Office
Entry level
Artificial Intelligence • Software
Open Source LLM Engineering Platform
The Role
Join Langfuse as a Customer Engineer to enhance customer experiences by solving technical issues, creating documentation, and supporting high-volume clients, contributing to product improvements with a focus on AI-driven solutions.
Summary Generated by Built In
About Langfuse
  • Open Source LLM Engineering Platform that helps teams build useful AI applications via tracing, evaluation, and prompt management (mission, product)

  • We have the chance to build the "Datadog" of this category; model capabilities continue to improve but building useful applications is really hard, both in startups and enterprises

  • Largest open source solution in this category: >2k customers, >14M monthly SDK downloads, >6M docker pulls

  • We raised money from world class investors (Y-Combinator, Lightspeed, General Catalyst) and are default alive by making more money than we spend

  • We're a small, engineering-heavy, and experienced team in Berlin and San Francisco (how we work)

About the Role

In Short: Langfuse is looking for a hands-on Technical Support Engineer to join the Berlin Office who will own the front line of our customer/community experience, keep a tight feedback loop with product engineering, and help thousands of AI‑product teams succeed with tracing & evals.

What you will be working on

At Langfuse support is not a cost‑center but a product discovery engine (see handbook): engineers own their features end-to-end, fixes land fast, and users ideally see “magic” when their feedback ships the same day. You will be running that engine while engaging with our large OSS community.

Community and low-touch Customer Support (70%)
  • Triage & solve inbound questions from diverse channels (Slack Connect, email, GitHub, Discord)

  • Improve Docs, snippets and auto‑reply prompts so the next 1000 teams self‑serve the answer → ideally users do not have questions and our product/docs are self-explanatory

  • Create helpful content i.e. helpful blog posts, sample repos and tutorials

  • Escalate bugs to product engineering and close the loop with the customer once shipped

  • Spin up proof‑of‑concepts (TypeScript/Python) to reproduce edge cases or demonstrate work‑arounds

Key Accounts (30%)
  • Act as named technical contact for a handful of high‑volume customers; jump on Slack huddles, unblock integrations, surface best practices

  • Track their feature requests, quantify impact, and champion them on our roadmap

  • Go through deep technical integration/implementation questions together with our customers and in their codebase

Who we are looking for

Must:

  • You deeply care about customers and want to help other engineers

  • Solid foundations in software engineering (CS or Data Science degree or comparable projects)

  • Strong writing: short Slack messages and long‑form guides/documentation

  • Curiosity for LLM application development

Bonus:

  • Past B2B solutions engineering, technical support, or DevRel

  • Open‑source maintainer

Why Langfuse?
  • You are joining really early

  • We have the greatest OSS community ❤️

  • Strong Product Market Fit & revenue growth!

  • We are building a very small but highly capable team

  • Only hiring engineering in foreseeable future

  • Building for very sophisticated technical users

  • Competitive salary & equity

  • Zero‑bureaucracy budget for learning

  • Team that pushes you

  • Everyone on the team says: "best place I've ever worked at"

How we work
  • Engineering based in Berlin Mitte ( we aim for 3-5 days in the office ) + optional SF trips

  • Only 2 scheduled meetings per week

  • Extreme ownership & autonomy

Hiring Process

We can run this process in less than 7 days.

  • Intro call (30 min) – meet a founder, talk motivation & logistics

  • Functional deep‑dive (45 min) – talk about your experience that maps to this role, what makes you excellent and the perfect fit

  • Implement a Demo Project (Take Home Challenge, expected time 3h) We will share with you a small implementation challenge to use Langfuse

  • Present your Demo Project (60min) – present your demo project, answer some followup questions and share product feedback with us.

  • On‑site Super‑Day (½ day) – we simulate a couple of core activities of this role during a mini-work-trial in our office to make both sides go from “Yes” to “Hell Yes”, during this day you will also get to meet the whole team

  • Decision & Offer – usually within 24 h.

We are very public about our strategy and product thinking, so feel free to start some deep research threads on this. Throughout the process, there will be ample time for any questions you may have that haven't already been addressed. We are generally super transparent, don't hesitate to ask any of the interviewers.

Links
  • All repos: github.com/langfuse

  • Company handbook: langfuse.com/handbook

  • Support Handbook: https://langfuse.com/handbook/support/support

  • Product Ops: https://langfuse.com/handbook/product-engineering/product-ops

  • How we hire: langfuse.com/handbook/how-we-hire

  • Blog: langfuse.com/blog

Top Skills

AI
Discord
Git
Python
Slack
Typescript
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The Company
HQ: San Francisco, California
15 Employees
Year Founded: 2022

What We Do

Langfuse is the 𝗺𝗼𝘀𝘁 𝗽𝗼𝗽𝘂𝗹𝗮𝗿 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗟𝗟𝗠𝗢𝗽𝘀 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺. It helps teams collaboratively develop, monitor, evaluate, and debug AI applications.

Langfuse can be 𝘀𝗲𝗹𝗳-𝗵𝗼𝘀𝘁𝗲𝗱 in minutes and is battle-tested and used in production by thousands of users from YC startups to large companies like Khan Academy or Twilio. Langfuse builds on a proven track record of reliability and performance.

Developers can trace any Large Language model or framework using our SDKs for Python and JS/TS, our open API or our native integrations (OpenAI, Langchain, Llama-Index, Vercel AI SDK). Beyond tracing, developers use 𝗟𝗮𝗻𝗴𝗳𝘂𝘀𝗲 𝗣𝗿𝗼𝗺𝗽𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁, 𝗶𝘁𝘀 𝗼𝗽𝗲𝗻 𝗔𝗣𝗜𝘀, 𝗮𝗻𝗱 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 to improve the quality of their applications.

Product managers can 𝗮𝗻𝗮𝗹𝘆𝘇𝗲, 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲, 𝗮𝗻𝗱 𝗱𝗲𝗯𝘂𝗴 𝗔𝗜 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 by accessing detailed metrics on costs, latencies, and user feedback in the Langfuse Dashboard. They can bring 𝗵𝘂𝗺𝗮𝗻𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽 by setting up annotation workflows for human labelers to score their application. Langfuse can also be used to 𝗺𝗼𝗻𝗶𝘁𝗼𝗿 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗿𝗶𝘀𝗸𝘀 through security framework and evaluation pipelines.

Langfuse enables 𝗻𝗼𝗻-𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝘁𝗲𝗮𝗺 𝗺𝗲𝗺𝗯𝗲𝗿𝘀 to iterate on prompts and model configurations directly within the Langfuse UI or use the Langfuse Playground for fast prompt testing.

Langfuse is 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 and we are proud to have a fantastic community on Github and Discord that provides help and feedback. Do get in touch with us!

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