Machine Learning Engineer, Local Search & Marketplace

Experience building machine learning, data mining, search, recommendation, ranking, NLP, or related systems through academic projects, internships, or industry work.

Search, retrieval, or learning-to-rank

Query understanding, intent classification, or NLP

Matching, marketplace optimization, or dispatch

Interest in building production systems that serve real users and deliver measurable product impact.

Experience deploying and maintaining machine learning models in production.

$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.

The US base salary range for this full-time position is listed below. Pay may vary based on a number of factors including job-related skills, level, experience, geographic location and relevant education or training. At NewsBreak, we design our overall rewards package to attract top talents. Depending on the position, the role may also be eligible for discretionary bonus and options. Your recruiter can share more details during the hiring process.

Annual Base Pay Range

$190,000

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.

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.