Staff Machine Learning Engineer, Retrieval
Reddit · Remote - United States
About this role
**Reddit — Staff Machine Learning Engineer, Retrieval** **Team Description** The Ads Retrieval ML team builds the machine learning systems that identify relevant advertising candidates for Reddit users. Retrieval sits at the heart of the ads delivery funnel: before downstream ranking and auction decisions, our models determine which campaigns and ads are eligible to compete. We work on large-scale retrieval across multiple objectives, placements, and geographies—combining representation learning, candidate generation, nearest-neighbor search, behavioral and contextual signals, and rigorous offline and online experimentation. **Role Description** We are looking for a Staff Machine Learning Engineer to provide technical leadership for the Retrieval ML team. You will lead the design and evolution of retrieval models and modeling practices that improve relevance, advertiser outcomes, and user experience at Reddit scale. This is an applied ML role centered on retrieval modeling and end-to-end product impact. You’ll stay close to the technical details—from data and objective design through model development, evaluation, experimentation, and launch—while setting direction for other engineers. **Responsibilities** - 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 (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, balancing recall, relevance, freshness, diversity, coverage, latency, and cost trade-offs. - Establish strong evaluation practices connecting retrieval metrics (recall, precision, candidate coverage, calibration) to downstream lift and ads/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. **Required Qualifications** - 7+ years of industry experience, including substantial experience building and shipping applied ML products. - Deep experience with information retrieval, candidate generation, recommender systems, ranking, or related relevance problems. - Strong understanding of retrieval modeling concepts (DNN, embeddings, two-tower/dual-encoder models, approximate nearest-neighbor search, multi-stage retrieval). - Deep experience training, evaluating, debugging, and deploying deep learning models using TensorFlow, PyTorch, or similar frameworks. - Demonstrated ownership of ML projects from problem framing and data preparation through offline evaluat
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