• Embeddings, vector search and ranking models
• Production ML systems and inference pipelines
• Strong foundations in machine learning and modern neural architectures.
• Hands-on experience building and deploying ML models in production.
• Experience in classification, ranking, retrieval, recommendation systems or personalization.
• Understanding of model evaluation, experimentation and performance metrics.
• Comfortable writing production-quality code and working with real-world datasets.
• Able to balance model quality, latency, cost and reliability.
• Strong ownership, problem-solving skills
• ML models in production meet expected accuracy, relevance, latency and reliability targets.
• Classification and ranking improvements deliver measurable gains in model quality.
• Evaluation pipelines identify regressions and support reliable model releases.
• Training, inference and data pipelines are robust, reproducible and maintainable.
• Model improvements are driven by real-world feedback and measurable results.
• Collaborates effectively with engineering and product teams to deliver reliable ML-powered features.
• The best products in the world are built by small, world-class teams. We are a high-talent-density, hands-on team. We make decisions collectively and move quickly, balancing high-quality work with continuous learning.