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# Machine Learning Engineer

- **Company:** Intapp
- **Location:** Berlin, Germany
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Data Deduplication, Information Engineering, Graph Database, Machine Learning, Parsing, Large Language Models, Web Content, Data Pipelines
- **Published:** September 2, 2026
- **Apply:** https://www.adzuna.de/details/5863019069

## About the Role

* Strong foundations in NLP and applied ML, with hands-on experience shipping systems to production
 * Familiarity with agentic patterns (tool use, multi-step reasoning, retrieval-augmented systems) and the tradeoffs that come with them
 * Experience working with large-scale, web-sourced data - crawling, parsing, deduplication, entity resolution, or similar
 * Comfort operating across the ML stack: data pipelines, model training and evaluation, and inference infrastructure
 * Collaborative and direct - you write clean code, communicate clearly, and have opinions you can defend

Nice to have:

 * Experience with knowledge graph construction or entity-centric data models
 * Familiarity with LLM evaluation frameworks and production monitoring
 * Background in domains where data quality is a core product quality signal

## Description

Intapp's ML team builds the data and intelligence layer that powers products used by professional firms across legal, accounting, consulting, and private capital. We're looking for an ML engineer to work on the systems that collect, enrich, and structure data from the web at scale - and to bring that same rigor to the ML components embedded across our product suite.

What you'll do:

 * Design and maintain NLP pipelines that extract, classify, and enrich structured knowledge from unstructured web content
 * Build and operate agentic workflows that automate data collection, validation, and maintenance with minimal human-in-the-loop overhead
 * Collaborate with product and data engineering teams to ship ML components that improve the quality and reliability of our data-driven products
 * Evaluate and integrate frontier models where they reduce complexity or improve coverage, with a bias toward production stability over novelty
 * Own the full lifecycle of your systems - from design through deployment, monitoring, and iteration


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