Senior Recommendation Engineer

About Nearby AI

The problem you will own

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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