Staff Machine Learning Engineer (Retrieval)

Join Reddit as a Staff Machine Learning Engineer (Retrieval) and lead the design and evolution of retrieval models that improve relevance, advertiser outcomes, and user experience. You will work closely with engineering, product, data science, and ads stakeholders to define the technical direction and multi-year roadmap for ads retrieval modeling. This is an applied ML role centered on retrieval modeling and end-to-end product impact.

• Lead the design and evolution of retrieval models and modeling practices to improve relevance, advertiser outcomes, and user experience.
• Define the technical direction and multi-year roadmap for ads retrieval modeling in partnership with engineering, product, data science, and ads stakeholders.
• Establish strong evaluation practices that connect retrieval metrics to ads and user outcomes, and lead offline analysis and online experiments.

• Deep experience training, evaluating, debugging, and deploying deep learning models using TensorFlow, PyTorch, or similar frameworks
• Experience working with large-scale behavioral, contextual, or content datasets and complex feature pipelines
• Technical leadership experience: setting direction, leading complex projects, influencing partner teams, and mentoring other engineers
• Strong command of experimental design and model evaluation, including how offline retrieval metrics relate to downstream business and user metrics
• Strong understanding of retrieval modeling concepts, including DNN, embeddings, two-tower or dual-encoder models, approximate nearest-neighbor search, and multi-stage retrieval
• Deep experience with information retrieval, candidate generation, recommender systems, ranking, or related relevance problems
• Strong software engineering fundamentals and the ability to write clear, reliable, maintainable production code
• Excellent written and verbal communication, with the ability to explain complex modeling choices to technical and non-technical audiences
• 7+ years of industry experience, including substantial experience building and shipping applied ML products
• Demonstrated ownership of ML projects from problem framing and data preparation through offline evaluation, online experimentation, production launch, and iteration
• Experience with sequential modeling (e.g., RNNs, Transformers)
• Publications, patents, or industry contributions in applied ML or ranking systems
• Experience connecting retrieval improvements to downstream ranking, auction, conversion, revenue, or user-experience outcomes
• Experience with ads retrieval, ad serving, recommendation, search relevance, or marketplace optimization
• Experience modeling user, content, campaign, or ad interactions with sequential, graph, or multimodal signals
• Experience in ads marketplaces at peer companies