Senior Machine Learning Scientist
Coursera
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Job Overview:
We are seeking a 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 relevant, personalized, and context-aware recommendations — redefining the learning experience on our platform. In addition to helping build a robust recommendations 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