About the Role
Join a small, early-stage AI team building a recommendation system for personalized advertising. You will own the matching stack end to end, from data and models through real-time serving, and help shape the system's technical priorities as it scales.
What You'll Do
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Build retrieval, ranking, and re-ranking systems to match ads with recipients.
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Develop embedding and vector-search infrastructure for real-time ad selection.
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Optimize ranking across click-through rate, advertiser return, publisher revenue, and subscriber relevance.
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Own the feature store, offline training, online inference, serving path, and pipeline health.
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Apply large language models and foundation models to subscriber tagging, content understanding, and user modeling.
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Take initiatives from prototype to production, including online learning, feedback loops, ETL/ELT pipelines, and warehouse transformations.
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Create dashboards and automated scorecards to track performance and key metrics.
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Partner with company leadership and engineering to set priorities, with an opportunity to grow into leading an ML team.
What We're Looking For
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At least 3 years of experience with recommendation, ranking, or advertising systems, and 5 to 16 years of overall experience.
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Production experience owning recommendation or matching systems across candidate generation, ranking, embedding-based retrieval, serving, and evaluation.
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Experience building ad ranking, click-through or conversion prediction, or ad-matching models.
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Hands-on experience with embeddings and real-time vector search tools such as FAISS, Milvus, Pinecone, pgvector, or similar.
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Strong SQL skills and experience building data pipelines and transformation layers with dbt or similar tools, plus Postgres or columnar analytical databases.
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Early-stage startup or founder experience is valued; experience building an ML team and graduate study in computer science or machine learning are also relevant.
Compensation & Benefits
Base salary: $200,000 to $300,000 annually. Visa sponsorship is not available.
Location
Hybrid, with preference for candidates in the New York City area or in a nearby U.S. time zone who can travel to New York City quarterly.
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