Job Overview:

At Coursera, our Machine Learning  team is helping to build the future of education through AI such as the natural language process, computer vision, or generative models. We define, develop, and launch the models that power content discovery, personalized learning, machine translation, skill tagging, 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 machine learning  team are positioned to make that vision a reality.

Responsibilities:

• Prototyping and developing state-of-the-art machine learning algorithms

• Drive Proof of Concepts (PoC) to explore high impact ML opportunities

• Responsible for ML model deployment, model QA, ML maintenance/monitoring, and the optimization of model runtime performance and scalability in production.

• Work with Product and Business stakeholders to understand customer needs and translate them into ML problems.

• Works with Data Engineering and Product Engineering teams to ensure we have the right data, tools and infrastructures in place to deploy ML models in production.

• Set up project priorities, manage projects deadlines, and ensure projects deliverables

Basic Qualifications:

• MS or Ph.D in in Computer Science, or related area with 3 Years minimum Machine Learning Scientist or Engineer industry experience

• Experience with Python, Java, SQL

• Knowledge in machine learning, computer vision, natural language processing, etc.

• Experience with at least one deep learning framework (e.g., TensorFlow, PyTorch, Caffe, MxNET, etc) 

• Experience deploying ML services and applications to at least one major cloud platform (AWS).

Preferred Qualifications:

• Experience with MLOps 

• Experience with CI/CD/CT pipelines, integrated tests, and microservice architectures such as RESTful web-services

• Experience with containerization such as Docker and Kubernates 

• Able to effectively deliver findings and recommendations to non-technical stakeholders in a clear and compelling fashion

• Experience with contributing machine learning community by publishing papers in the top tier conferences such as CVPR, ICCV, ACL, EMNLP, KDD, ICML, NeruIPS, etc 

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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