Staff Machine Learning Engineer (Retrieval)

• We are looking for a Staff Machine Learning Engineer to provide technical leadership for the Retrieval ML team
• You will lead the design and evolution of retrieval models and modeling practices that improve relevance, advertiser outcomes, and user experience at Reddit scale
• This is an applied ML role centered on retrieval modeling and end-to-end product impact
• You will be expected to stay close to the technical details—from data and objective design through model development, evaluation, experimentation, and launch—while setting direction for other engineers
• Define the technical direction and multi-year roadmap for ads retrieval modeling in partnership with engineering, product, data science, and ads stakeholders
• Design, develop, and launch candidate-generation and retrieval models for campaigns and ads across Reddit’s advertising surfaces
• Apply modern approaches such as two-tower architectures, representation learning, embeddings, sequence models, graph-based methods, and other deep learning techniques when they create meaningful product value
• Improve the retrieval stack across key modeling decisions, including objectives, labels, sampling strategies, hard-negative mining, feature design, embedding generation, candidate filtering, and retrieval depth
• Work with approximate nearest-neighbor and vector retrieval systems, reasoning about recall, relevance, freshness, diversity, coverage, latency, and cost trade-offs
• Establish strong evaluation practices that connect retrieval metrics—such as recall, precision, candidate coverage, calibration, and downstream lift—to ads and user outcomes
• Lead offline analysis and online experiments, interpret ambiguous results, and translate findings into the next modeling iteration
• Partner with downstream ranking, ads platform, auction, measurement, and product teams to ensure retrieval models integrate effectively into the full ads funnel
• Write design documents, review code and model changes, and raise the quality bar for modeling, testing, observability, and production ownership
• Mentor ML engineers and help grow the team’s expertise in retrieval, recommendation, and representation learning

Benefits

• Comprehensive health benefits
• Flexible vacation & Reddit global days off
• Family planning funds & 4+ months paid parental leave
• Personal & professional development funds
• Paid volunteer time off
• Workspace & home office benefits- 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