Your playing field at Gini:

  • Your mission is to build magical and high-quality systems for information extraction from documents and thus help our team in their pursuit to revolutionize accounting
  • You gather, explore and analyze data as well as execute prototyping experiments
  • You train machine learning models to extract relevant document information
  • You implement new strategies (e.g. employ secondary data, leverage visual information, …) to make our extraction system even smarter
  • You help optimizing Gini’s product by contributing high-quality code
  • You are a specialist in your field that is not afraid to help out with relevant team tasks
  • You are a hands-on person – you like solving challenges and contributing to new development tasks

Your profile:

  • You burn for machine learning/deep learning and have experience with relevant toolkits (scikit-learn, Tensorflow, Keras, PyTorch, …)
  • Your communication and analytical skills are superb and you quickly immerse yourself into new topics, terminologies and developments
  • You take ownership for your projects and are persistent and efficient when it comes to finding the best solutions
  • You have programmed in a team before and manage to keep a positive attitude in a fast-paced environment
  • You own a Statistics, Computer Science or related degree and have proven machine learning experience with real-world NLP problems
  • You’re fluent in English and have a passion for working with language data
  • You know Java/Scala and Python and are an expert in at least one of them

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About Gini GmbH

Gini is dedicated to putting an end to paperwork. Voted as FinTech Startup of the Year 2015, it uses artificial intelligence to capture, structure and extract data from documents for further usage. Based on that, Gini provides smart solutions on how to get paperwork done with minimal effort, allowing customers to use their time for things that really matter in life. Gini’s photo payment is already a standard in the German online banking market, used by millions of banking customers.