Apply With This Link: https://boards.greenhouse.io/coursera/jobs/4986133004
Job Overview
We are seeking a pioneering Staff Machine Learning Scientist (Recommendations) to join our Discovery Science ML team at Coursera, focusing on creating the next generation of hyper-personalized recommender systems. The candidate will play an instrumental role in researching and developing state-of-the-art techniques for personalized, context-aware recommendations—redefining the learning experience on our platform. In addition to building multi-stage recommender systems, this role requires keeping abreast of emerging trends and innovations in machine learning, recommender systems, and online education.
Responsibilities:
• Design, develop, and maintain advanced multi-stage recommender systems, drawing from ML techniques such as embedding based retrieval, reinforcement learning, online learning, and LLM’s
• Explore and implement sequential recommender architectures, graph-based recommender systems, domain tuned LLM’s in the RecSys space, and knowledge graph embeddings to improve personalization.
• Build and optimize scalable user preference embeddings, utilizing large feature spaces and training deep networks for personalized ranking and re-ranking.Incorporate contextual information, such as device type, user behavior, time of day, and geolocation, to provide real-time, hyper-personalized recommendations.
• Collaborate with cross-functional teams to align research goals with business needs and ensure the successful deployment of innovative solutions into production.
• Stay up-to-date with the latest trends in ML, recommender systems, and online education, frequently attending conferences, workshops, and engaging in collaborative research projects.
• Contribute to Coursera's research efforts by publishing in top-tier conferences like RecSys, KDD, WWW, Sigir, and similar.
Basic Qualifications:
• PhD or Master's degree in Computer Science, AI, or closely related fields.
• Demonstrated experience in developing advanced recommender systems, incorporating techniques like reinforcement learning, transfer learning, and unsupervised learning.
• Background in working with user preference embeddings, large feature spaces, and sequential recommender architectures, such as Transformers.
• Track record of publishing research in top-tier conferences like RecSys, KDD, WWW, Sigir, or similar.
Preferred Qualifications:
• Proficiency in programming languages, such as Python, and deep learning frameworks like TensorFlow or PyTorch.Familiarity with ML deployment in production environments and tools for version control, such as Git.
• Proven ability to stay current with emerging research and technologies in the ML and recommender systems domain.
• Experience collaborating with cross-functional teams and excellent communication abilities.
• Passion for driving impact in the field of online education through innovative machine learning and personalization techniques.
• Familiarity with Coursera's platform and course offerings, as well as active participation in wider AI and Machine Learning communities, is a plus
