Staff Machine Learning Engineer, Retrieval & Targeting

What you'll do

• Define the technical direction and multi-year roadmap for ads retrieval modeling.
• Design, develop, and launch candidate-generation and retrieval models for various advertising surfaces.
• Apply deep learning techniques like two-tower architectures, representation learning, and graph-based methods.
• Optimize the retrieval stack including objectives, labels, sampling strategies, and hard-negative mining.
• Manage approximate nearest-neighbor and vector retrieval systems while balancing recall, latency, and cost.
• Establish evaluation practices connecting retrieval metrics to downstream ads and user outcomes.
• Lead offline analysis and online experiments to interpret results and iterate on models.
• Mentor ML engineers and grow the team’s expertise in retrieval and recommendation systems.

What we're looking for

• 7+ years of industry 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 including DNNs, embeddings, two-tower models, and approximate nearest-neighbor search.
• 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 production launch and iteration.
• Strong command of experimental design and model evaluation relating offline retrieval metrics to downstream business and user metrics.
• Experience working with large-scale behavioral, contextual, or content datasets and complex feature pipelines.
• Strong software engineering fundamentals and the ability to write clear, reliable, maintainable production code.
• Technical leadership experience including setting direction, leading complex projects, influencing partner teams, and mentoring engineers.
• Excellent written and verbal communication skills to explain complex modeling choices to technical and non-technical audiences.
• Experience with ads retrieval, ranking, targeting, serving, or marketplace optimization (preferred).
• Experience modeling interactions with sequential, graph, or multimodal signals (preferred).
• Experience connecting retrieval improvements to downstream ranking, auction, conversion, revenue, or user-experience outcomes (preferred).
• Experience in ads marketplaces at peer companies (preferred).
• Publications, patents, or industry contributions in applied ML or ranking systems (preferred).
• Experience with sequential modeling such as RNNs or Transformers (preferred).