Despre companie

Veridium has been working for years on reducing or eliminating identity as a major attack vector, replacing the vulnerable password with biometric authentication. Powered by an unmatched legacy of biometrics R&D, Veridium…

Strada Buzești 71, Bucharest, Romania
Alte stagii în Artificial Intelligence
Toate stagiile în Artificial Intelligence

Machine Learning Engineer Intern

Stagiu plătit la VeridiumID

Veridium mission is to provide a modern authentication platform, adopted for new industrial revolution – digitalization.

Bucharest is part of Veridium global network (New York, Boston, London and Bucharest) and it acts as a technical headquarter, responsible for all research and development aspects (data scientist, data engineering, software engineering, information technology operation, and quality assurance).

Our team innovates technologies which are subject to US patents and global recognition (“Cutting Edge Biometrics InfoSec Award for 2019” – RSA Conference)

We are looking now for highly-motivated, forward-thinking talents to add to our growing Machine Learning team.

You will learn as many of the following knowledge and knowing some of them represents an advantage:

  • Knowledge of different types of machine learning algorithms (SVM, Kernel Ridge Regression, Random Forest, PCA, k-means, etc.) and know how to use them in practice
  • Good understanding of neural network theory and how to train, test and evaluate modern architectures, e.g. convolutional neural networks, recurrent neural networks, auto-encoders, etc.
  • Ability to combining different types of models and architectures in order to improve the performance.
  • Knowledge of tuning models’ parameters, e.g. using grid search.
  • Feature engineering. Being able to understand the data that you are working with and extract useful features.
  • Statistics and probability: Know how to evaluate a model or a solution in terms accuracy, precision, recall or other performance metrics. Understand probabilistic models like Naive Bayes, Hidden Markov Models, ROC curves, etc
  • Signal processing: Use different types of signal processing methods for extracting relevant features, e.g. Discrete Fourier Transform.
  • Read and understand different scientific papers in order to find ideas that can be applied to our solution.
  • Being able to implement the solution in a distributed environment, e.g. Spark and Kafka.
  • Knowledge of python and working experience with libraries such as tensorflow, keras, scikit-learn, numpy, pandas.

General description

  • To research and develop solutions for user behavior analysis

Ideal candidate:

  • Very good algorithms and data structures knowledge;
  • Final year in BS/MS in Computer Science represents a plus;
  • Experience with Python