Job Description

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 129 million registered learners as of June 30, 2023.

Coursera partners with over 300 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.

At Coursera, our Data team is helping build the future of education through data-driven decision making and data-powered products. We drive product and business strategy through measurement, experimentation, and causal inference. We define, develop, and launch the ML/AI models and algorithms that power content discovery, personalized learning, and machine-assisted teaching and grading. We believe the next generation of teaching and learning should be personalized, accessible, and efficient. With our scale, data, technology, and talent, Coursera and its Data team are positioned to make that vision a reality.

As a Staff Machine Learning Engineer, you will work closely with our Machine Learning Scientists, Data Engineers and Engineers to build ML infrastructures and internal tools to accelerate and power ML products development processes. You will be responsible for model deployment and model optimization of runtime performance and scalability in production. Your focus in this role would be to help ML scientists iterate faster and work more efficiently which includes building tools that could enable auto-retrain and auto-deployment services. As a senior member of the team, you are expected to collaborate with cross functional stakeholders and VP of Data to establish a strategy on how to scale ML/AI applications in production and furthermore, to provide technical mentorship to junior ML scientists in the team.

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 ML Engineers and act as a technical leader for the ML engineering domain

Basic Qualifications:

• 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:

• Solid understanding in machine learning theory and practice, and experience using machine learning tools (Scikit-Learn, TensorFlow, Weka 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

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

Compensation:

Our job titles may span more than one career level. The starting base pay for this role is between $118,000 and $219,000. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs, and location. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, and benefits.

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Coursera is an Equal Employment Opportunity Employer and considers all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, age, marital status, national origin, protected veteran status, disability, or any other legally protected class.

If you are an individual with a disability and require a reasonable accommodation to complete any part of the application process, please contact us at accommodations@coursera.org.

For California Candidates, please review our here.

For our Global Candidates, please review our here.

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