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Senior Backend Engineer (Data Infrastructure)

Langfuse
CompanyLangfuse
CategoryEngineering
LocationEurope
RemoteHybrid
EmploymentNot stated
LevelSenior
SalaryEUR 90k–160k
Posted17 Feb 2026
Last verified30 Jul 2026
SourceEmployer career page (ashby)
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Description
ABOUT LANGFUSE Open Source LLM Engineering Platform that helps teams build useful AI applications via tracing, evaluation, and prompt management (mission https://tracking.us.nylas.com/l/6d586a21a6fc4e1a8aacc7eb75882b72/0/82383757e54352130f65066e1b2fc4708aacab7897561bcb8000fe4c8a9c6a21?cache_buster=1761124921, product https://tracking.us.nylas.com/l/6d586a21a6fc4e1a8aacc7eb75882b72/1/b9fba3a93b6ffcc0f99ecda62767a17cc437fe8fe0b16181d1c43c1391212e3d?cache_buster=1761124921). We are now part of ClickHouse. We're building 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: trusted by 19 of the Fortune 50, >2k customers, >26M monthly SDK downloads, >6M Docker pulls. We joined ClickHouse in January 2026 because LLM observability is fundamentally a data problem and Langfuse already ran on ClickHouse. Together we can move faster on product while staying true to open source and self-hosting, and join forces on GTM and sales to accelerate revenue. Previously backed by Y Combinator, Lightspeed, and General Catalyst. We're a small, engineering-heavy, and experienced team in Berlin and San Francisco. We are also hiring for engineering in EU timezones and expect one week per month in our Berlin office (how we work https://langfuse.com/handbook/how-we-work/principles). In Short: We're looking for a backend/platform engineer to own and scale the infrastructure behind the most widely adopted open source LLM engineering platform — processing terabytes of AI tracing data per day for thousands of AI teams, including 19 of the Fortune 50. WHY BACKEND ENGINEERING AT LANGFUSE Your work will have an outsized impact. Langfuse ingests terabytes of AI tracing data per day. When a Fortune 50 company instruments their application with Langfuse, they hit the infrastructure you built. Everything you ship is open source and immediately visible. The core of your role is making Langfuse fast and affordable at scale: owning the ingestion pipeline, optimizing ClickHouse data models and queries so dashboards load instantly, and building the backend abstractions that let product engineers ship data-heavy features without reinventing the plumbing each time. You will also make sure Langfuse runs everywhere. You’ll operate Langfuse Cloud across multiple production environments, and make self-hosting effortless. You will support a single Docker Compose setup to enterprise-grade Helm chart deployment. You won’t be solving these problems alone. Langfuse is now part of ClickHouse, which means the people who built the database you’re optimizing are one channel away. Few backend roles give you that kind of access to the internals of your core datastore. YOU WILL GROW AT LANGFUSE BY - Own and optimize our ingestion pipeline: Langfuse ingests massive volumes of tracing data through a pipeline that flows from our API to ClickHouse. You'll work on throughput, latency, reliability, and cost efficiency at every stage of this pipeline. - Make ClickHouse fast: our tracing data lives in ClickHouse. You'll design schemas, optimize merge strategies, tune batch inserts, write and review analytical queries that power dashboards and the UI. You'll become an expert on ClickHouse internals — and you'll have the ClickHouse database engineering team one channel away. - Build new infrastructure-heavy features: things like LLM-as-a-Judge (where we post-process large volumes of ingested data asynchronously), alerting at scale, client-code execution, or batch dataset operations. Build the foundation and, if you like, dip your toes into the frontend for those. - Test with real data in production: when evaluating a new ClickHouse schema, migration, or query optimization, you'll run load tests against production data to validate that changes actually improve things. We don't guess at performance. We prototyp
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