Machine Learning Scientist

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 162 million registered learners as of September 30, 2024.

Coursera partners with over 350 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. 

Coursera has a commitment to enabling flexibility and workspace choices for employees. Our interviews and onboarding are entirely virtual, providing a smooth and efficient experience for our candidates. As an employee, we enable you to select your main way of working, whether it's from home, one of our offices or hubs, or a co-working space near you.

Job Overview:

We are seeking a Machine Learning Scientist to join our Discovery Science ML team at Coursera, focusing on creating the next generation of personalised search and recommender  systems. The candidate will play an instrumental role in researching and developing state-of-the-art techniques for relevant, personalized, and context-aware search and recommendations — redefining the learning experience on our platform. In addition to helping build a robust IR system, this role requires keeping abreast of emerging trends and innovations in machine learning, information retrieval,  and online education.

Responsibilities:

• Design, develop, deploy, and maintain advanced recommendations ranking models, leveraging machine learning techniques such as two tower models, natural language processing (NLP), label collection, learning-to-rank, user behavior analysis, & LLMs
• Collaborate with cross-functional teams to align research goals with business needs and ensure successful deployment of innovative solutions into production.
• Build and manage large-scale datasets, including corpora, relevance labels, and user interactions, utilizing tools and techniques for data collection, cleaning, and preprocessing.
• Conduct thorough evaluations of recommendations  models using industry-standard metrics, analyze results, and provide insights for model improvement and business strategy.
• Stay up-to-date with the latest trends in ML, recommender systems, search science, and information retrieval, frequently attending conferences, workshops, and engaging in collaborative research projects.
• Contribute to Coursera's research efforts by publishing in top-tier conferences such as SIGIR, WWW, CIKM, and similar venues.

Basic Qualifications:

• PhD or Master's degree in Computer Science, Information Retrieval, or closely related fields.
• Demonstrated experience in developing advanced recommendations  models, incorporating techniques like natural language processing (NLP) and learning-to-rank algorithms.
• Familiarity with information retrieval metrics, evaluation methodologies, and scalable search system architecture.
• Track record of publishing research in top-tier conferences such as SIGIR, EMNLP, WWW, CIKM, or similar venues.

Preferred Qualifications:

• Proficiency in programming languages and deep learning frameworks such as Python, TensorFlow, or PyTorch.
• Experience in working with large-scale datasets and tools for data collection, cleaning, and preprocessing.
• 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 recommendations domain.
• Experience with MLOps, ML engineering
• Experience collaborating with cross-functional teams and excellent communication abilities.
• Passion for driving impact in the field of online education through innovative ML and recommendations techniques.
• Familiarity with Coursera's platform and course offerings, as well as active participation in wider AI and Machine Learning communities, is a plus.
• Familiarity with data science concepts, including the ability to design, implement, and analyze A/B tests in an online environment to optimize product performance and user experience.

If this opportunity interests you, you might like these courses on Coursera:

• Unsupervised Learning, Recommenders, Reinforcement Learning
• Recommender Systems: Evaluation and Metrics  

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