Machine Learning Engineer II

About Coursera
Coursera was founded in 2012 by Stanford professors Andrew Ng and Daphne Koller to make world-class learning accessible to everyone, everywhere. Today, over 190 million learners and 375+ university and industry partners use our platform to gain skills in fields like AI, data science, technology, and business. As a Delaware public benefit corporation and Certified B Corp, we're driven by the belief that learning can transform lives through learning.

Why Join Us
At Coursera, we're looking for inventors, innovators, and lifelong learners ready to shape the future of education. You'll help build global programs and tools that power online learning for millions turning bold ideas into real impact. People who thrive here are customer-first builders who move fast, simplify ruthlessly, and iterate relentlessly on the metrics that matter.

We're a globally distributed team and let you choose the best way you work, whether it's from home, a Coursera hub, or a co-working space near you. Our virtual hiring and onboarding make it easy to join us and start making an impact from anywhere. If you're ready to make a global impact, scale unique products exclusive to Coursera, and expand your career horizons, apply below.

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

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 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 in Computer Science, or related area with 1 Years minimum Machine Learning Engineer industry experience or Ph.D in in Computer Science, or related area
• Understanding in machine learning theory and practice, and experience using machine learning tools (Scikit-Learn, TensorFlow, PyTorch etc.)
• Understanding and experience working with cloud-based solutions, especially AWS, Databricks
• 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:

• Machine Learning Engineering for Production (MLOps) Specialization
• Computer Vision for Engineering and Science Specialization
• Natural Language Processing Specialization

Coursera is an Equal Opportunity Employer committed to building a welcoming and inclusive workplace. We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request at Learn more in our CCPA Applicant Notice and GDPR Recruitment Notice.