To build the digital sales assistant, we utilize the most recent state of the art algorithms in information retrieval, machine learning and data processing to provide information about construction projects to our customers in the way they need it, when they need it.
At Building Radar, machine learning is more than just an add-on but defines the core of our technology. Your mission as machine learning engineer is to utilize the latest developments and most powerful algorithms in machine learning and deep learning to unify any online information about a construction project into a structured resume, using Python, Spacy, Prodigy, Tensorflow and of course your repertoire of deep learning techniques.
Apply to Building Radar and work with us on a product that has an impact on the daily work life of thousands of people, join a great team and revolutionize a €72 billion dollar industry.
+ Understanding business objectives and developing models that help to achieve them
+ Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
+ Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
+ Verifying data quality, and/or ensuring it via data cleaning
+ Training models and tuning their hyperparameters
+ Analyzing the errors of the model and designing strategies to overcome them
+ Deploying models to production
+ Strong theoretical background: You have a deep understanding of how and why machine learning works and can apply this knowledge in practice
+ You have a track record of successful machine learning or data science projects that go far beyond MNIST
+ Challenging problems motivate you to exceptional performance: If you run into unexpected issues, you dig deeper until you discover the (maybe unconventional) approach to solve the problem
+ Customer orientation: Your main goal is to build great products for our customers, even while working on complex and abstract machine learning projects