Senior Recommendation Engineer

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 problem you will own

Most recommendation systems optimize for the next click. Ours has to support an infrequent, expensive, high-consequence household decision, where the right recommendation is sometimes to do nothing. You will build recommendation and matching across three surfaces: which content to surface to which user and when in a large content feed; an event-triggered engagement system that responds to changes in a user's situation; and two-sided matching between customers and service professionals under explicit fairness constraints. Ranking is never for sale, and the objective functions you design must reflect that.

Responsibilities

• Build and own the recommendation and ranking systems for feed surfaces, from candidate generation through online experimentation and monitoring.
• Design the event-triggered engagement engine with the messaging platform team: condition detection, propensity and uplift modeling, frequency management, and holdouts.
• Build customer-to-professional matching, starting with transparent rules and evolving to learned ranking, with an explicit fairness and exposure contract so new, high-quality providers can win work.
• Define evaluation frameworks that treat downstream outcomes and negative signals, including complaints and "do not proceed" recommendations, as first-class objectives.
• Contribute learnings back to the company's pricing and intent models owned by the AI team.

Qualifications

Required

• 5 years of machine-learning or software engineering, including 3 years shipping recommendation or ranking systems in production.
• Built and operated a ranking system at consumer scale, millions of users, including candidate generation, ranking, and monitoring.
• Substantial online experimentation experience on ranking changes, and can explain at least one test that failed and why.
• Deployed an uplift, causal, or counterfactual model in production, or can give a rigorous account of why click-based objectives are wrong for rare, high-cost decisions.
• Strong Python plus at least one of Java, Scala, or Go; production experience with feature stores and streaming pipelines.
• Ability to explain modeling decisions to product and business stakeholders and to defend experimental design under commercial pressure.

Preferred

• Feed or notification ranking at a content or media company.
• Two-sided marketplace matching with fairness or exposure constraints in production.
• Domains with low-frequency, high-consequence decisions such as insurance, healthcare, or real estate.
• Publications or open-source contributions in recommendation, causal inference, or marketplace design.

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