Напиши текст вакансии для должности "Graph Analytics Engineer". Название компании: "Intel Corporation". Расположение: Москва. График работы: Удаленная работа. Опыт работы: От 3 до 6 лет. Ключевые навыки: 'C/C++', 'Mathematical Statistics', 'Mathematical Programming', 'Graph analytics area', 'Machine Learning', 'Python'

Fully remote work is available. Intel® oneAPI Data Analytics Library (oneDAL) team is looking for a Graph Analytics Machine Learning Engineer. oneDAL is a powerful machine learning library for data scientists that turns large data clusters into meaningful information with advanced analytics algorithms and is highly optimized for modern Intel® hardware architectures. With the growth of models, data sets, and also computation resources availability at scale it is crucial to have capability to distribute workload across multiple devices and/or machines. Classic Machine Learning is used more often in practice than deep learning for many applications like ranking, classification, regression, prediction. Intel® is one of the contributors for the most popular frameworks and libraries in this space and puts effort into their efficiency by leveraging oneDAL. In this role you will be responsible for exploration, implementation, and optimizations of Graph Analytics algorithms (Page Rank, Jaccard Similarity, Subgraph Isomorphism, and more) for modern Intel architectures (CPU and GPU), making them scalable, and enable their application to the real customers’ cases and frameworks. You may also develop and publish case studies and articles exposing the benefits of the algorithms and the integrated optimizations. The team will be glad to bring you onboard and help you to learn new technologies in machine learning and data analytics. You will have a unique opportunity to work in the most promising and rapidly developing area of artificial intelligence and become one of the firstest who provide powerful analytics for really big data. Qualifications  Experience in C++ including templates and latest standards knowledge.  Strong mathematical/stat background. Basic knowledge in machine learning and graph analytics.  The following qualifications would be an added advantage:  Experience with parallelism in shared (Intel TBB, OpenMP) and distributed (MPI, SHMEM, Apache Spark, Dask) memory. Experience in Python. Knowledge of OpenCL/SYCL/CUDA languages.