Staff Machine Learning Engineer (Retrieval & Targeting)

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.

Responsibilities

• 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 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

Requirements

• Deep experience with information retrieval, candidate generation, recommender systems, ranking, or related relevance problems
• 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

Preferred

• 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)