Machine Learning Engineer

Intapp

. Design and maintain NLP pipelines that extract, classify, and enrich structured knowledge from unstructured web content .

Posted 9/22/2026full-timeBerlin • GermanyMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining NLP pipelines and agentic workflows, with a strong foundation in applied machine learning and data quality improvement. Proficient in managing the full lifecycle of systems, from design to deployment and monitoring.

Highest-signal resume keywords

NLP Pipeline DesignApplied Machine LearningData Pipeline ManagementEntity ResolutionModel Training and Evaluation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

NLPMachine LearningData CrawlingData ParsingDeduplicationEntity ResolutionKnowledge Graph ConstructionLLM Evaluation FrameworksProduction MonitoringClean Code Writing

Soft Skills

Clear Communication

Industry Keywords

Agentic PatternsMulti-Step ReasoningRetrieval-Augmented SystemsData QualityWeb-Sourced Data

About the role

Key responsibilities & impact

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 data quality and reliability
Evaluate and integrate frontier models where they reduce complexity or improve coverage, prioritizing production stability
Own systems through their full lifecycle, from design and deployment to monitoring and iteration

Requirements

What you’ll need

Strong foundations in NLP and applied ML, with hands-on experience shipping systems to production
Familiarity with agentic patterns, including tool use, multi-step reasoning, and retrieval-augmented systems
Experience working with large-scale, web-sourced data, including crawling, parsing, deduplication, and entity resolution
Comfort operating across the ML stack, including data pipelines, model training and evaluation, and inference infrastructure
Ability to write clean code and communicate clearly
Experience with knowledge graph construction or entity-centric data models is nice to have
Familiarity with LLM evaluation frameworks and production monitoring is nice to have
Background in domains where data quality is a core product quality signal is nice to have

Benefits

Comp & perks

Reimbursement for training and continuing education
Modern, open offices
Complimentary lunches
Fully stocked kitchens
Comprehensive wellness programs
Flexible time off programs
Family-formation benefits
Paid volunteer time off
Donation matching program
Professional growth opportunities
Collaborative and welcoming culture