Staff Machine Learning Engineer (Retrieval & Targeting)

Join Reddit's Ads Retrieval ML team as a Staff Machine Learning Engineer. In this role, you will lead the design and evolution of retrieval models and modeling practices that improve relevance, advertiser outcomes, and user experience. You will work on large-scale retrieval across multiple objectives, placements, and geographies, and apply modern approaches such as two-tower architectures, representation learning, and deep learning techniques. You will also establish strong evaluation practices, lead offline analysis and online experiments, and partner with downstream ranking, ads platform, auction, measurement, and product teams.

• Definir la dirección técnica y la hoja de ruta a largo plazo para el modelado de recuperación de anuncios en colaboración con equipos de ingeniería, producto, ciencia de datos y publicidad.
• Diseñar, desarrollar y lanzar modelos de generación de candidatos y recuperación para campañas y anuncios en las diversas superficies publicitarias de Reddit.
• Establecer prácticas de evaluación sólidas que conecten las métricas de recuperación, como el recuerdo, la precisión, la cobertura de candidatos, la calibración y el aumento posterior, con los resultados de anuncios y usuarios.

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