Full Stack Machine Learning Scientist
To see similar active jobs please follow this link: Remote Development jobs
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:
#LI-CP1
Full Stack Machine Learning Scientist
To see similar active jobs please follow this link: Remote Development jobs
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:
#LI-CP1