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 148 million registered learners as of March 31, 2024.
Coursera partners with over 325 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:
At Coursera, our Machine Learning team plays a crucial role in shaping the future of education through cutting-edge AI technologies such as natural language processing, computer vision, and generative models. We are dedicated to defining, developing, and launching models that drive content discovery, personalized learning, machine translation, skill tagging, and machine-assisted teaching and grading. Our vision is centered on creating a next-generation education experience that is personalized, accessible, and efficient. Leveraging our scale, extensive data, advanced technology, and talented team, Coursera is poised to transform this vision into reality.
Responsibilities:
- Work very closely with ML scientists and help them with model deployment in the production systems
- Work very closely with ML scientists to find and solve engineering pain-points by building scalable, general-use platforms
- Build scalable and reliable infrastructure and pipelines for data/feature processing and storage and also scalable training and evaluation infrastructure and pipelines to accelerate model development
- Automate ML workflows to enhance productivity across training, evaluation, testing, and results generation
- Partner with cross functional stakeholders to define a long-term vision for scaling ML/AI applications in production and help teams with their roadmap plannings
- Provide technical mentorship to junior other Engineers and act as a technical leader for the ML engineering domain
Basic Qualifications:
- BS in Computer Science, or related area with 3 Years minimum Machine Learning Scientist or Engineer industry experience
- Highly skilled with Java development, Python and SQL/MySQL.
- Highly skilled with strong proficiency in ML ops with experience in building large-scale ML applications, services, pipelines and architecture
- Solid understanding and experience in system design of ML systems (design pattern, OOD, architecture, modules, interfaces, etc)
- Highly skilled with distributed processing architecture and ML/data workflow management platform (Spark, Databricks, Airflow, Kubeflow, MLflow etc)
- Experience with containerization such as Docker and Kubernates
Preferred Qualifications:
- MS or Ph.D in in Computer Science, or related area with 3 Years minimum Machine Learning Scientist or Engineer industry experience
- Solid understanding in machine learning theory and practice, and experience using machine learning tools (Scikit-Learn, TensorFlow, PyTorch etc.)
- Solid understanding and experience working with cloud-based solutions, especially AWS
- Knowledge in c++ or c# would be also preferred.
- Experience with CI/CD pipelines, integrated tests and test-driven development
- Experience with microservice architectures such as RESTful web-services
- Experience with contributing to the machine learning community through paper publications in top-tier conferences such as CVPR, ICCV, ACL, EMNLP, KDD, ICML, NeurIPS, etc., or involvement in open-source communities like Hugging Face.
If this opportunity interests you, you might like these courses on Coursera:
Machine Learning Engineering for Production (MLOps) Specialization
Computer Vision for Engineering and Science Specialization
Natural Language Processing Specialization
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