Senior Technical Account Manager

Langfuse

1d

About The Position

Open Source LLM Engineering Platform that helps teams build useful AI applications via tracing, evaluation, and prompt management (mission, product). 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).
Your impact
Make our best customers successful in production and expanding over time.
Improve net revenue retention via adoption, outcomes, and proactive risk management.
Scale your impact to our large user base and OSS community by contributing to documentation, guides, and other public content.
Create a tight loop from “what customers do” (your deep understanding of top customers) → “what we should build” (feedback to the product engineering team) → “how the GTM org explains it.” (GTM enablement).

Requirements

• Senior experience in a customer-facing technical role: TAM, Solutions Engineer, Solutions Architect, Forward Deployed Engineer, Customer Success Engineer, or similar where you owned outcomes.
• Strong technical foundation: you can debug integrations, reason about distributed systems, APIs/SDKs, and cloud infrastructure.
• Demonstrated work in applied AI / AI engineering: building, operating, or enabling LLM applications (agents, RAG, eval pipelines, prompt tooling, experimentation).
• Excellent communication: you can lead technical meetings, drive decisions, and write docs engineers actually follow.
• High ownership: you ship artifacts, close loops, and create repeatable systems rather than bespoke one-offs.

Nice To Haves

• Experience with devtools / OSS ecosystems and developer-centric GTM.
• Familiarity with observability concepts (tracing/metrics/logs), data pipelines, and evaluation frameworks.
• Track record of technical writing or enablement (workshops, reference architectures, public docs).

Responsibilities

• Own strategic customer relationships (portfolio ownership)
• Be the primary technical partner for 10–20 strategic accounts (large, highly engaged, or aligned with our roadmap).
• Run onboarding, success planning, and regular deep dives into the customer’s AI architecture and workflows.
• Drive adoption of key product capabilities across the lifecycle: initial setup, team workflows, scaling, and expansion.
• Production readiness + architectural guidance
• Lead customers through production readiness: instrumentation strategy, data modeling choices, evaluation setup, alerting/monitoring expectations, security & privacy considerations, and operational playbooks.
• Provide pragmatic architecture guidance for real LLM systems (agents, tool use, RAG, evals, prompt iteration, dataset curation, feedback loops).
• Build small prototypes, reference implementations, and demos when it unblocks a customer. Turn them into reusable templates that can be published.
• Escalation leadership
• Own the technical leadership during high-severity customer moments: triage, root-cause coordination, and crisp communication.
• Be the point of contact for the customer and partner closely with Engineering, be proactive in how you resolve issues.
• Establish escalation paths, runbooks, and prevention mechanisms for repeat issues.
• Turn customer signal into product + docs + enablement
• Aggregate patterns across your portfolio and translate them into actionable product feedback (clear problem statements, impact, and recommended solutions).
• Create customer-facing assets (docs, guides, best practices, demos) that start as one customer’s question and become durable collateral.
• Enable the broader ClickHouse GTM org: training, playbooks, crisp messaging, and “how to win” narratives for AI engineering teams.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed