- Location
- Mountain View, California, United States
- Type
- Internship
- Department
- Core Product
- Seniority
- Internship
- Education
- Bachelor
- Source
- Greenhouse
Description
About NewsBreak
Founded in 2015, NewsBreak is the Content Intelligence platform shaping the future content economy. With over 40 million monthly active users, our flagship platform delivers highly personalized local news and information powered by advanced AI, recommendation systems, and adtech.
Recognized by Fast Company as #32 on the Top Workplaces for Innovators, we're proud to be Great Place to Work® certified and home to a dynamic team of technologists, product innovators, and business leaders who are passionate about solving meaningful challenges at scale.
Together, we reached unicorn status in 2021, and we remain committed to continuing this high-growth trajectory with the right team to fulfill our mission: building the infrastructure layer for content intelligence.
If you’re inspired to dream big, innovate fast, and make a difference, we’d love to hear from you! For more information, visit www.newsbreak.com/about
About Nearby AI
Nearby AI is a new company from NewsBreak building a trust-first marketplace for local home services. We help homeowners understand a problem, what it should reasonably cost, and whether to hire at all before they are connected to a provider, and we help service providers win work on fit and outcomes rather than on speed of contact.
Our mission: give people clarity and control from the first sign of a problem to a job done right, and give good contractors work they are equipped to win.
We are a small founding team. We work from evidence, keep a written record of decisions, and hold a short set of product principles we do not trade for revenue: no pay-for-rank, no sharing of customer contact information beyond what the customer approved, and no quality claims we cannot substantiate.
The role
Help build the personalized provider-recommendation pipeline end to end: user understanding with LLMs, provider retrieval and ranking, backstage agent workflows, multi-source knowledge integration, and evaluation, working with engineers and product managers from design through launch.
- User understanding and personalization: use LLMs to extract service requirements, preferences, and constraints from conversations, profiles, and context
- Provider retrieval and ranking: combine semantic search, recommendation algorithms, and provider features (capabilities, coverage, transaction-backed reputation, user fit) to improve relevance and coverage
- Agent workflows: build backstage pipelines connecting intent understanding, retrieval, provider comparison, and recommendation decisions, with explanations a user can check
- Multi-source data and knowledge integration: entity matching across business information, reviews, credentials, and service coverage
- Evaluation and delivery: build evaluation datasets, analyze failure cases and user feedback, improve quality, reliability, and latency; ship and iterate
Qualifications
Minimum:
- Pursuing a bachelor's degree or higher in Computer Science, AI, or a related field.
- Proficient in Python or Java with solid data structures and algorithms.
- Hands-on experience developing LLM applications or agents (RAG, agent workflows, evaluation harnesses).
- Understanding of search, recommendation, or ML fundamentals.
- Strong problem-solving and communication; initiative to own results.
Preferred:
- Embeddings, hybrid search, reranking, learning to rank, or personalized matching.
- Elasticsearch, MongoDB, PostgreSQL, vector databases, or data pipelines.
- Shipped products, open-source contributions, or independent projects showing individual impact.
- Research and personal projects count; GitHub links and demos welcome.