Machine Learning Engineer, Local Search & Marketplace

About the Role

We are looking for a Machine Learning Engineer to build intelligent systems that connect consumers with relevant local information, businesses, and services.

You will work across areas such as search, recommendation, ranking, retrieval, user-intent understanding, personalization, and marketplace matching. Depending on your background and interests, you may focus on understanding consumer demand, improving search and recommendation relevance, or building models that more effectively match users with local supply.

This is a hands-on role with the opportunity to take machine learning solutions from problem definition and experimentation through production deployment and iteration. We welcome candidates across a range of experience levels, and the scope and level of the role will be calibrated based on the candidate’s experience.

What You’ll Do

• Build and improve machine learning models for search, recommendation, ranking, retrieval, matching, and personalization.
• Develop systems that understand user queries, behaviors, preferences, and context.
• Apply embeddings, natural language processing, large language models, and modern retrieval techniques to connect consumer demand with relevant local content, businesses, or services.
• Build and optimize end-to-end ML pipelines, from data preparation and model training to online serving and monitoring.
• Partner with product, engineering, and data teams to define problems, identify opportunities, and translate business needs into scalable ML solutions.
• Design and analyze online and offline experiments to measure model quality and product impact.
• Improve key product outcomes such as relevance, engagement, conversion, retention, and marketplace efficiency.
• Explore new ML and LLM techniques and bring them into production where they can deliver measurable value.

What We’re Looking For

• Experience building machine learning, data mining, search, recommendation, ranking, NLP, or related systems through academic projects, internships, or industry work.
• Strong programming skills in Python, Java, C++, or another relevant language.
• Solid understanding of machine learning fundamentals, data structures, algorithms, and statistical analysis.
• Experience with one or more of the following:
  • Search, retrieval, or learning-to-rank
  • Recommendation or personalization
  • Query understanding, intent classification, or NLP
  • Embeddings, semantic search, or large language models
  • Matching, marketplace optimization, or dispatch
• Ability to work with large-scale or complex datasets and translate ambiguous problems into practical solutions.
• Strong collaboration and communication skills.
• Interest in building production systems that serve real users and deliver measurable product impact.

Nice to Have

• Experience deploying and maintaining machine learning models in production.
• Experience with experimentation, including A/B testing, causal analysis, or marketplace experiments.
• Familiarity with deep learning frameworks and ML infrastructure, such as PyTorch, TensorFlow, Spark, Kubernetes, or feature and model-serving platforms.
• Experience building taxonomies, user-interest representations, knowledge graphs, or behavioral models.
• Experience applying LLMs to search, recommendation, classification, or information retrieval.
• Background in local search, local services, maps, commerce, marketplaces, delivery, mobility, or location-based products.
• Experience at a consumer internet, search, recommendation, advertising, e-commerce, or marketplace company.

Annual Base Pay Range

$190,000 - $300,000

The US base salary range for this full-time position will vary based on job-related skills, level, experience, geographic location, and relevant education or training. 

At NewsBreak, we design our overall rewards package to attract top talent. Depending on the position and level, the role may also be eligible for a discretionary bonus and equity. Your recruiter can share more details during the hiring process.