Coursera is hiring for an entry level Remote Machine Learning Scientist role in India

Position

• Machine Learning Scientist

Company

• Coursera

Location

• India

Job Type

• Full-time

Job Mode

• Remote

Job Requisition ID

• Not specified

Years of Experience

• Not explicitly mentioned; as per JD: 0-3 years
  

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Company Description

• Coursera, founded in 2012 by two Stanford Computer Science professors, Andrew Ng and Daphne Koller, is a globally recognized leader in online learning.
• The platform aims to provide universal access to world-class education, making learning opportunities accessible to individuals worldwide.
• With a staggering 162 million registered learners as of September 2024, Coursera has established itself as one of the largest online education platforms globally.
• Partnering with over 350 top universities and industry leaders, Coursera offers a vast range of learning solutions, including individual courses, Specializations, Professional Certificates, Guided Projects, and full bachelor’s and master’s degree programs.
• The organization also supports businesses, governments, and educational institutions in reskilling and upskilling their teams in high-demand fields such as data science, technology, and business.
• Coursera became a certified B Corporation in 2021, reflecting its commitment to balancing purpose and profit by positively impacting society through education.
• With a strong focus on diversity and inclusion, Coursera embraces employees from across the globe, provided they meet working eligibility requirements and share overlapping time zones with their teams.
• As an employee-centric organization, Coursera ensures a flexible work environment, allowing individuals to choose between working from home, co-working spaces, or office hubs.

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Profile Overview

• Coursera is seeking a highly motivated and skilled Machine Learning Scientist to join its Discovery Science ML team.
• This role focuses on developing advanced technologies for personalized search and recommendation systems that enhance user experiences on the platform.
• The primary responsibility involves researching and deploying state-of-the-art machine learning models for context-aware, personalized, and relevant search and recommendation functionalities.
• The role requires an innovative mindset to design next-generation recommender systems and contribute to building a robust Information Retrieval (IR) system.
• Collaboration with cross-functional teams is integral to aligning technical innovations with business goals and ensuring successful deployment of solutions into production.
• The position involves managing large-scale datasets, ensuring effective data preprocessing, and leveraging tools to derive actionable insights for model improvement.
• Continuous learning and staying updated with the latest trends in machine learning, search science, and recommender systems are essential for success in this role.
• The selected candidate will actively contribute to Coursera’s research community by publishing findings in reputable conferences such as SIGIR, WWW, and CIKM.

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Qualifications

Basic Qualifications

• Possession of a Master’s or PhD in Computer Science, Information Retrieval, or a related discipline.
• Proven experience in developing advanced recommendation models using cutting-edge techniques like Natural Language Processing (NLP) and learning-to-rank algorithms.
• Strong familiarity with information retrieval metrics, scalable search systems, and evaluation methodologies.
• Demonstrated ability to publish research findings in top-tier conferences, such as SIGIR, EMNLP, WWW, or CIKM.

Preferred Qualifications

• Proficiency in programming languages like Python and familiarity with deep learning frameworks such as TensorFlow or PyTorch.
• Extensive experience in handling large-scale datasets, including tasks like data collection, cleaning, and preprocessing.
• Knowledge of deploying machine learning models in production environments and utilizing tools for version control, like Git.
• Ability to stay updated with emerging technologies and research developments in machine learning and recommendation systems.
• Experience in MLOps and ML engineering, ensuring seamless integration of machine learning models in operational workflows.
• Proven ability to collaborate effectively with cross-functional teams, ensuring alignment of technical and business objectives.
• Passion for making a tangible impact in online education through innovative applications of machine learning.
• Familiarity with Coursera’s platform and active participation in the broader AI and machine learning community is considered a plus.
• Understanding of data science concepts, including designing and analyzing A/B tests to optimize product performance and user experience.

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Additional Info

• The Machine Learning Scientist role at Coursera involves hands-on work in developing recommendation ranking models using advanced techniques such as two-tower models, label collection, and large language models (LLMs).
• The position requires proficiency in building and managing large datasets, with an emphasis on corpora, user interactions, and relevance labels, ensuring high-quality data preprocessing.
• Strong emphasis is placed on conducting detailed evaluations of recommendation models using industry-standard metrics, analyzing results, and implementing improvements.
• The role encourages participation in conferences and collaborative research projects, with opportunities to publish findings in globally recognized venues.
• Coursera provides an inclusive hiring process, ensuring equal employment opportunities regardless of race, religion, gender, age, or other legally protected statuses.
• Flexible work arrangements allow employees to choose their preferred working mode, whether remote, office-based, or co-working.
• Candidates are encouraged to explore related courses on Coursera, such as “Unsupervised Learning, Recommenders, Reinforcement Learning” and “Recommender Systems: Evaluation and Metrics,” to understand the foundational concepts used in the role.
• Coursera’s interview and onboarding processes are fully virtual, ensuring a seamless candidate experience from start to finish.

Please click here to apply.

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