Employer description & source details
Define the technical direction and multi-year roadmap for ads retrieval modeling in partnership with engineering, product, data science, and ads stakeholders. Design, develop, and launch candidate-generation and retrieval models for campaigns and ads across Reddit’s advertising surfaces. Apply modern approaches such as two-tower architectures, representation learning, embeddings, sequence models, graph-based methods, and other deep learning techniques when they create meaningful product value. Improve the retrieval stack across key modeling decisions, including objectives, labels, sampling strategies, hard-negative mining, feature design, embedding generation, candidate filtering, and retrieval depth. Work with approximate nearest-neighbor and vector retrieval systems, reasoning about recall, relevance, freshness, diversity, coverage, latency, and cost trade-offs. Establish strong evaluation practices that connect retrieval metrics—such as recall, precision, candidate coverage, calibration, and downstream lift—to ads and user outcomes. Lead offline analysis and online experiments, interpret ambiguous results, and translate findings into the next modeling iteration. Partner with downstream ranking, ads platform, auction, measurement, and product teams to ensure retrieval models integrate effectively into the full ads funnel. Write design documents, review code and model changes, and raise the quality bar for modeling, testing, observability, and production ownership. Mentor ML engineers and help grow the team’s expertise in retrieval, recommendation, and representation learning. 7+ years of industry experience, including substantial experience building and shipping applied ML products. Deep experience with information retrieval, candidate generation, recommender systems,
Extracted skills: machine learning, artificial intelligence