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Applied Scientist / Machine Learning Engineer

Confidential company · Berlin · Posted Jul 10, 2026

Public summary

Join a leading global consumer tech company operating across multiple countries, working on advanced machine learning models that enhance search relevance and personalized content discovery for millions of customers. Collaborate with cross-functional teams including engineers and product managers to develop models from research through to production, impacting business metrics and customer experience at scale. Opportunities available in tech hubs across Europe with remote work options and relocation support. Diverse and inclusive work environment fostering growth and innovation.

Location and work setup

Location
Berlin
Remote status
Remote
German requirement signal
No German Required Detected
Detected job language
English

Responsibilities

Design and develop machine learning models for search relevance, query understanding, and ranking across multiple markets. Implement state-of-the-art solutions to improve customer experience and business metrics. Handle end-to-end ML workflows including problem framing, data analysis, model development, offline evaluation, and production monitoring. Collaborate closely with engineers, product managers, and analysts to translate research into impactful applications. Contribute to the applied science community through knowledge sharing and improving ML practices.

Qualifications

4+ years of hands-on experience in applied machine learning with proven production deployment, or a PhD in machine learning with applied research experience. Solid expertise in search technologies including query understanding, intent prediction, or semantic search. Proficiency in Python and experience with ML frameworks and large-scale data processing. Strong communication skills for collaborating across cross-functional teams. Experience with NLP, dense retrieval, learning-to-rank, or embedding-based methods is a plus.

Skills

Applied Machine Learning Search Relevance Query Understanding Ranking Algorithms Python ML Frameworks Large-scale Data Processing Natural Language Processing (NLP) Dense Retrieval Learning-to-Rank Embedding-based Methods Collaboration Technical Communication