Machine Learning Lead


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Your mission

You will
  • lead a small team of machine learning scientists
  • take care of software projects, i.e. communicate with the customer, but also take decisions on how to tackle problems
  • do software quality assurance
  • code and be creative yourself

Your profile

You have
  • Master’s or ideally a PhD in mathematics, physics or computer science
  • at least two years of industry experience
  • experience in leading small (software) teams
  • a creative mind that likes to solve problems
  • a firm knowledge of modern machine learning approaches (including deep learning)
  • solid programming experiences (Python is a bonus)

About us

dida is a machine learning software company with exciting problems for instance in computer vision and natural language processing. Our team tackles applied problems for different customers by using latest scientific advancements (especially in deep learning) and therefore believes that research oriented thinking can help solving real-world problems more efficiently.

Why us?

You will meet an interdisciplinary team of people with a solid background in mathematics and statistics. We offer flexible working hours and have a nice office with good coffee in Berlin Schöneberg. We believe in science and support our team in publishing their research results.

Find below a short description of two of our current projects.

Estimate the amount of solar panels that fit on a roof (computer vision): 
Given a satellite picture and a ground image of a house, automatically detect certain elements of a roof (including obstacles, dormers etc.) in order to find out how many solar panels fit on it. This involves inferring 3d information from 2d pictures in order to infer the roof pitch.

Detect, classify and suggest legal effectiveness of text paragraphs (NLP): 
Automatically go through thousands of legal documents with the goal to classify dedicated paragraphs and check their legal effectiveness. This involves converting scans to text, coming up with a labelling scheme (problem modelling), and detecting different paragraphs automatically, before tackling the inference task.