NewsBreak
Machine Learning Engineer, Local Search & Marketplace
About this role
NewsBreak is seeking a Machine Learning Engineer to develop intelligent systems connecting users with relevant local information, businesses, and services. You'll work on search, recommendation, ranking, and personalization models, taking solutions from ideation through production deployment in a fast-growing content intelligence platform.
What you'll do
- Build and improve ML models for search, recommendation, ranking, retrieval, matching, and personalization
- Develop systems that understand user queries, behaviors, preferences, and context
- Apply embeddings, NLP, LLMs, and modern retrieval techniques to connect demand with local content
- Build and optimize end-to-end ML pipelines from data prep through online serving and monitoring
- Design and analyze online and offline experiments to measure model quality and product impact
- Partner with product, engineering, and data teams to translate business needs into scalable ML solutions
What they're looking for
- Machine learning model development
- Python, Java, C++, or similar programming languages
- Search, retrieval, and learning-to-rank systems
- Natural language processing and large language models
- Embeddings and modern retrieval techniques
- ML pipeline development and deployment
- Experimental design and statistical analysis
- Recommendation systems and ranking algorithms
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NewsBreak
NewsBreak operates a news and local marketplace platform serving 40M+ monthly active users, leveraging machine learning and data infrastructure to power recommendations, advertising, and matching between consumers and businesses. The company is hiring software engineers, ML engineers, and data specialists to build and optimize backend systems, ML infrastructure, recommendation engines, and algorithms that drive user engagement and monetization.
- Website
- newsbreak.com
Likely interview questions
- Tell us about a machine learning project you've built from problem definition through production—what challenges did you face?
- How would you approach improving search relevance for a local marketplace with diverse user intents?