.Paris; Barcelona;A betterinternet, where privacy is the default, is possible. Building this better internet might seem daunting or even unthinkable, but at Proton, this is what we do every day.Proton was founded in 2014 by a group of scientists who met at the European Organization for Nuclear Research (CERN). Our first product, Proton Mail, is now the world's largest encrypted email service. Subsequent Proton products, such as Proton VPN, Proton Calendar, Proton Drive and Proton Pass give our users full control over how and with whom their data is shared.Today, Proton makes privacy universally accessible. Journalists from outlets like The Guardian and The New York Times, some of the world's largest organizations, and people in more than 180 countries have signed up for over 100 million accounts. Our diverse and dynamic team is made up of more than 500 members representing over 40 different nationalities. While we are based in Geneva, Switzerland, we have offices in Zurich, Prague, London, Vilnius, Skopje, Taipei, Paris, Barcelona and many more employees working remotely around the world.Join one of Europe's fastest-growing companies to help us solve challenging problems and build new products that will reach hundreds of millions of people. We want to create more than just one of the world's most impactful tech companies; we want to create a newinternet that serves the interests of all people. We need you, your voice, your ideas, and your ambition to make it happen.Purpose of the roleWhat you will doAs a Machine Learning Engineer at Proton Pass, you'll handle HTML form and field detection, a key feature of our product.Model Design: figuring out the best models (k-NN, SVM, MLP, ...) appropriate to the various detection tasks inside the DOM, keeping in mind speed as an absolute priority due to the interactive nature of the Pass extension.Feature engineering: transposing HTML attributes, DOM relationships into vectors or other intermediate representations.Model Evaluation: deciding on relevant metrics to reliably track efficacy, establishing binary classification matrices, ROC/PR curves, and reducing experimental noise with statistical validation like cross-validation/bootstrapping.ML Ops: ensuring continuous delivery of new models and streamlining the process to get new models in production with little delay.Job requirementsDon't worry if you don't tick every box—apply anyway! We're happy to help you grow in some areas.Bachelor's degree or higher in Computer Science, Statistics, or a related field.Strong grasp of ML techniques like classification, regression, and outlier detection.Experience or familiarity with the DOM API is a big plus.Experience with modern ML frameworks (like PyTorch, TensorFlow, or Jax).Who You AreYou can explain technical concepts in a way that anyone can understand. People love working with you.You're endlessly curious and love figuring out how systems and models work under the hood
