Senior Machine Learning Scientist (Discovery)

Key Skills

Aartificial intelligence, Deep Learning, Machine learning techniques, Natural Language Processing (NLP), Python Programming, PyTorch, TensorFlow

Job Description

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

Responsibilities:

• Design, develop, and maintain advanced search ranking models, leveraging machine learning techniques such as label collection, personalized ranking, candidate retrieval, user behavior analysis, & LLM’s
• Explore and implement robust query understanding functionality, document understanding, user preference understanding, and low latency transformer-based architectures to improve search relevance
• Collaborate with cross-functional teams to align research goals with business needs and ensure successful deployment of innovative search solutions into production.
• Build and manage large-scale search datasets, including corpora, relevance labels, and user interactions, utilizing tools and techniques for data collection, cleaning, and preprocessing.
• Conduct thorough evaluations of search 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, search science, RecSys, 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 search models, recommender systems, 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 RecSys, 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 search 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 , search science, or 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 ML and search science 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

Compensation:

Coursera offers competitive pay and equitable compensation practices. Our job titles may span more than one career level. The targeted hiring base salary range for this role is between $134,000 - $200,00 for all Canada candidates. The actual base pay is dependent upon many factors, including but not limited to prior work experiences, training/education, transferable skills, business needs, and geographical location. The base pay range is subject to change and may be modified in the future. This role may also be eligible for variable pay, equity, and benefits.