(Москва, SAP) Machine Learning Internship
As market leader in enterprise application software, SAP helps companies of all sizes and industries innovate through simplification.
From the back office to the boardroom, warehouse to storefront, on premise to cloud, desktop to mobile device – SAP empowers people and organizations to work together more efficiently and use business insight more effectively to stay ahead of the competition.
SAP applications and services enable customers to operate profitably, adapt continuously, and grow sustainably.
SAP is 77 000 employees in 130 offices worldwide, including solid representation in Russia
№1 in the development and implementation of corporate information systems
Clients from more than 282.000 companies from 26 industries in 190 countries
98% of top 100 brands in the world RUN SAP
42 years of glorious history
Innovator in the field of big data, cloud computing and mobile platforms
What you will get during the fellowship as a benefit
  • A mentor from whom you can learn and to whom you can ask any time of questions to extend your knowledge in machine learning;
  • A real customer cases experience, practice in end-to-end realization including deployment in production;
  • And the most important part: It's not necessary to be a specialist in ML initially, but if during the internship you show zeal, diligence, and potential, the mentor will make every effort to recommend you to be part of the SAP ML team for further employment.
  • Bachelor's degree in a quantitative field (Machine Learning, Deep Learning, Statistics, Mathematics, Computer Science, or Physics);
  • Knowledge of at least one Deep Learning framework (such as Tensorflow/Keras/Caffe);
  • Knowledge of Python (NumPy, SciKit-Learn);
  • Docker and GitHub/git experience;
  • Machine Learning theory and algorithm understanding (SL, UL, RL), GPU's and languages such as OpenGL/CL, CUDA;
  • Understanding of SQL and NoSQL concepts;
  • SAP, Google or Amazon Cloud experience is a plus;
  • Active kaggle account is a plus.
  • Computer vision, NLP, Time series analysis;
  • Generative models and Reinforcement learning tasks using ML and DL.