Senior Recommendation Engineer (AI)

Location: Mountain View, California, United States

Annual base salary: $150,000–$300,000 USD, with potential discretionary bonus and options.

Company

hirify.global is a content intelligence platform serving more than 40 million monthly active users through personalized local news and information powered by AI, recommendation systems, and adtech. The role supports Nearby AI, a new trust-first marketplace for local home services.

What you will do

• Build and own recommendation and ranking systems for large-scale content feeds, from candidate generation through experimentation and monitoring.
• Design an event-triggered engagement engine covering condition detection, propensity and uplift modeling, frequency management, and holdouts.
• Develop customer-to-professional matching, evolving transparent rules into learned ranking with fairness and exposure constraints.
• Define evaluation frameworks that include downstream outcomes, complaints, and recommendations not to proceed as core objectives.
• Share recommendation and matching insights with the AI team’s pricing and intent models.

Requirements

• 5+ years of machine-learning or software engineering experience, including 3+ years shipping recommendation or ranking systems in production.
• Experience operating ranking systems for millions of users, including candidate generation, ranking, and monitoring.
• Strong online experimentation experience and the ability to explain failed tests and their causes.
• Production experience with uplift, causal, or counterfactual modeling, or a rigorous understanding of why click-based objectives fail 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 defend experimental design under commercial pressure.

Nice to have

• Feed or notification ranking experience at a content or media company.
• Production experience with two-sided marketplace matching and fairness or exposure constraints.
• Experience in low-frequency, high-consequence domains such as insurance, healthcare, or real estate.
• Publications or open-source contributions in recommendation, causal inference, or marketplace design.

Culture & Benefits

• Work on trust-first products designed to give homeowners clarity and control.
• Product principles include no pay-for-rank, limited sharing of customer contact information, and no unsupported quality claims.
• Rewards may include discretionary bonus and options in addition to base salary.
• Join a small founding team working from evidence and maintaining written records of product decisions.