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Join Coursera as they are looking for a Staff ML Scientist (Recommender Systems)
Overview
Salary No salary declared 😔
Location United States
Expires Expires at anytime
Coursera was launched in 2012 by two Stanford Computer Science professors, Andrew Ng and Daphne Koller, with a mission to provide universal access to world-class learning. It is now one of the largest online learning platforms in the world, with 136 million registered learners as of September 30, 2023.
Coursera partners with over 300 leading university and industry partners to offer a broad catalog of content and credentials, including courses, Specializations, Professional Certificates, Guided Projects, and bachelor’s and master’s degrees. Institutions around the world use Coursera to upskill and reskill their employees, citizens, and students in fields such as data science, technology, and business. Coursera became a B Corp in February 2021.
Join us in our mission to create a world where anyone, anywhere can transform their life through access to education. We're seeking talented individuals who share our passion and drive to revolutionize the way the world learns.
We at Coursera are committed to building a globally diverse team and are thrilled to extend employment opportunities to individuals in any country where we have a legal entity. We require candidates to possess eligible working rights and have a compatible timezone overlap with their team to facilitate seamless collaboration. As a remote-first company, our interviews and onboarding are entirely virtual, providing a smooth and efficient experience for our candidates.
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.
If this opportunity interests you, you might like these courses on Coursera:
- Unsupervised Learning, Recommenders, Reinforcement Learning
- Recommender Systems: Evaluation and Metrics
