Machine Learning Engineer

Company Summary Veridium is a leading provider of end-to-end biometric authentication solutions for enterprises deploying biometrics as part of their access and identity management security strategies. Powered by an unmatched knowledge of biometrics R&D, Veridium solutions increase convenience and security, reduce fraud, and cut costs associated with passwords and traditional multi-factor authentication.  We are looking for highly-motivated, forward-thinking talent to add to our growing team. VeridiumID platform it is composed by:
  • Server Application
  • Mobile App (Android and iOS)
  • Integration points with AD and Citrix infrastructure
  • Deployment scripts
  • User Behavior Authentication

Technologies

  • Mobile – native applications (Android and iOS)
  • VeridiumID Service – Java REST API
  • VeridiumID Admin Console (AngularJS application)
  • Persistence Layer / Data processing – Zookeeper, Kafka, Spark / Tensor Flow, Cassandra (+Lucene Index)
  • Deployment – Ansible Scripts / python
Requirements Min requirements:
  • MS in Computer Science/Electronics/Electrical Engineering or a related technical discipline
  • Master in Artificial Intelligence
  Having as many of the following skills 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

  • Research and develop solutions for user behavior analysis