Senior Machine Learning Engineer, Recommendation

Accountabilities: Build, optimize, and maintain recommendation and search systems across personalized feeds, discovery pages, search experiences, and content continuation features. Own retrieval and ranking pipelines, including candidate generation, embedding-based retrieval, two-tower models, ranking features, and online serving performance. Design, launch, and analyze end-to-end recommendation and search experiments, using quantitative insights to improve model quality and product outcomes. Improve discovery experiences for new users, newly published content, and rapidly evolving content catalogs by addressing cold-start challenges and changing user preferences. Develop meaningful user, content, creator, and session representations using behavioral signals and other relevant data sources. Partner with product, data, and engineering teams to establish success metrics, conduct experiments, and deliver measurable improvements in user engagement, retention, satisfaction, and content distribution. Build practical machine learning solutions that move efficiently from prototypes to production, supported by robust evaluation, monitoring, and performance optimization. Translate complex user behavior and product challenges into effective modeling strategies and actionable recommendations. Contribute to the long-term architecture and technical direction of AI-powered content discovery systems. Explore emerging approaches in recommendation, semantic search, personalization, and AI-driven content understanding to improve discovery quality and relevance. Requirements: At least 5 years of industry experience developing and deploying production machine learning systems, with senior-level ownership of recommendation, search, ranking, advertising ranking, feed ranking, or content discovery. Hands-on experience building recommendation or search systems for consumer-facing applications. Background working on entertainment, social, gaming, short-form content, creator platforms, or other engagement-driven consumer products. Strong practical expertise in two-tower models, embedding-based retrieval, candidate generation, ranking algorithms, and offline and online model evaluation. Strong product intuition and understanding of relevance, engagement, retention, user satisfaction, cold-start problems, and content distribution. Ability to transform complex and imperfect behavioral data into useful modeling signals and measurable product improvements. Solid engineering fundamentals spanning machine learning development, data pipelines, backend integration, experimentation frameworks, and production deployment. Demonstrated ownership, rapid execution, analytical thinking, and clear communication in ambiguous, fast-paced product environments. Ability to collaborate effectively across technical and product teams while independently driving complex projects from concept to launch. Preferred: Experience with AI-powered recommendations, LLM-based ranking, semantic search, personalized content generation, or AI-native content understanding. Preferred: Familiarity with user-generated content ecosystems, creator marketplaces, or rapidly changing content catalogs. Preferred: Experience with multimodal machine learning involving text, images, video, interaction data, or AI-generated content. Preferred: Knowledge of explore-exploit strategies, contextual bandits, reinforcement learning, or long-term user value optimization. Preferred: Startup experience or a track record of building machine learning systems from the ground up with limited infrastructure. Benefits: Competitive compensation: A strong overall compensation package. Equity opportunity: Meaningful equity participation. Healthcare coverage: Comprehensive health insurance and benefits. Remote-first flexibility: A remote-first working environment. Technical ownership: Opportunities to lead significant machine learning initiatives and influence product and architecture decisions. Innovation-driven culture: The opportunity to explore emerging AI technologies and develop new approaches to personalized discovery. High-impact work: The chance to shape consumer experiences at the intersection of artificial intelligence, social products, and interactive entertainment.