Staff Machine Learning Engineer, Retrieval

What you'll do

• Define the technical direction and multi-year roadmap for ads retrieval modeling across various stakeholders.
• Design, develop, and launch candidate-generation and retrieval models for campaigns and ads on multiple surfaces.
• Implement advanced deep learning techniques including two-tower architectures, representation learning, and sequence models.
• Optimize the retrieval stack through improvements in objective design, sampling strategies, feature engineering, and embedding generation.
• Manage approximate nearest-neighbor systems while balancing trade-offs between recall, latency, cost, and diversity.
• Establish evaluation frameworks that connect offline retrieval metrics to downstream business outcomes and user experience.
• Lead offline analysis and online experiments to interpret results and drive the next modeling iterations.
• Mentor machine learning engineers and provide technical leadership on complex projects and production standards.

What we're looking for

• 7+ years of industry experience building and shipping applied ML products.
• Deep experience in information retrieval, candidate generation, recommender systems, or ranking.
• Strong understanding of retrieval modeling including DNNs, embeddings, two-tower models, and approximate nearest-neighbor search.
• Deep experience training, evaluating, debugging, and deploying deep learning models using TensorFlow or PyTorch.
• Demonstrated ownership of ML projects from problem framing through production launch and iteration.
• Strong software engineering fundamentals and ability to write reliable, maintainable production code.
• Technical leadership experience including setting direction, leading complex projects, and mentoring engineers.
• Experience with ads retrieval, search relevance, or marketplace optimization (preferred); publications or patents in applied ML (preferred).