Daniel C. Ferreira

Daniel C. Ferreira

Machine Learning Engineer



I am a Machine Learning expert based in Vienna, Austria with a background in mathematics and working since 2016 in academia and industry. My focus is on applying ML techniques to Natural Language Processing and/or Cybersecurity topics.

I have experience in the full lifecycle of ML, from data collection/processing/labeling all the way to deployment, and like to work in projects where a little bit of everything is needed. My past professional experience includes a leading cybersecurity company, the Technical University of Vienna, among others.

Outside of work I’m passionate about music, and in particular metal (check out some of my work), and also video-games.

  • Artificial Intelligence
  • Cyber Security
  • Natural Language Processing
  • Blockchain
  • Music
  • MSc in Applied Mathematics, 2015

    Técnico Lisboa

  • BSc in Applied Mathematics and Computation, 2013

    Técnico Lisboa


Machine Learning Engineer
Oct 2022 – Present

Open to embarking on new challenging projects in NLP and cybersecurity. Feel free to reach out if you think I can help you :)

Also available via Toptal.

Jun 2019 – Sep 2022 Vienna, Austria

I tackled a multitude of ML problems in the intersection of NLP and cybersecurity.

I worked on topics such as website categorization (using multilingual text and images), DNS tunneling detection, and IoT security. Heavy emphasis on developing production-ready ML models, and following MLOps best practices. Using state-of-the-art Machine Learning models (e.g., Transformers).

Aug 2016 – Feb 2019 Vienna, Austria

I was part of the BigDAMA project, a fundamental research project focused on big data analytics for network traffic monitoring and analysis.

My research topics are mostly related to how to represent network traffic for detecting attacks at the network level (i.e., which features to use). Experimented with both classical features (what have people been using and why?), and feature learning approaches using Deep Learning techniques, and in particular representing traffic in 2-dimensional spaces.

Mar 2016 – Jul 2016 Lisbon, Portugal

I was part of the SUMMA project, an H2020 project for media monitoring with multiple industry and academic international partners.

I specifically tackled the problem of named entity recognition in which, given a text with some entity mentions (e.g., David and Victoria), the goal is to find the corresponding entities in Wikipedia (e.g., David Beckham and Victoria Beckham). We used both classical methods (such as SVM) and modern Deep Learning approaches.