Nearby AI Internship Program - Engineering Track
About NewsBreak
Local life runs on local information. What happened down the street? Who can fix this? Where should we go tonight? NewsBreak started in 2015 to answer these questions, and today more than 40 million people a month rely on us to follow what’s going on in their zip code.
Three platforms with one vision: powering local life. Led by founder and CEO Jeff Zheng, NewsBreak is our local life and news platform; Nearby AI connects people with local businesses; and NDot AI helps publishers and creators grow and earn.
Building for the long term. A $115 million Series C made us a unicorn in 2021, and we keep investing in our people. We’re Great Place to Work® certified and named to Fast Company’s Best Workplaces for Innovators (2025) and Fortune’s Best Workplaces in the Bay Area™ (2026).
Everyone makes an impact. Whether you write code, shape products, tell our story, or run the business, we want people who own projects end to end, stay curious, and speak up. Join us.
Learn more at: NewsBreak, Nearby AI, NDot AI
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.