Machine Learning Research Scientist (m/f/d)

  • Full- / Part- Time
  • Berlin, Germany

We are looking for a passionate and innovative Machine Learning (ML) research scientist with a strong background in Machine Learning, Mathematics or Physics. You will help to improve our AI quality toolbox “aidkit“ – in particular, you will develop new algorithms and strategies to evaluate the robustness and comprehensibility of state-of-the-art ML models. Furthermore, you will have the chance to work on exciting research projects with leading companies in various industries, for example in the automotive, telecommunication and health-care industry.

Your Tasks:

  • You track and analyze the status-quo of the literature in the field of ML quality evaluation, particularly adversarial robustness, corruption robustness, verification and explainable AI.
  • You support our engineering team in the complex task of integrating ML quality evaluation algorithms into our product “aidkit”.
  • Together with our customers and our research team, you develop new approaches to test and improve the robustness and comprehensibility of ML models.
  • You take an active role in transferring your ML testing experience to the AI standardization world, e.g. develop technical requirements for various DIN / ISO committees.

Your Profile:

  • MS/PhD in Mathematics, Optimization, Statistics, Computer Science, Machine Learning, Physics, or a related field.
  • Expertise with Python, particularly numpy, TensorFlow, Keras and pandas.
  • Experience in scientific writing, ideally own ML-related publications.
  • Strong communication, presentation skills and business fluent English (German is not required).
  • Knowledge of / exposure to ML applications, and robustness or comprehensibility algorithms.
  • Working with a high degree of self-responsibility.
  • Fun working in cross sectional team.

Nice to have:

  • Industry experience in ML, particularly Neural Network development and testing.
  • Software Engineering experience, ideally with application to performance engineering in a ML context.
  • Strong background in optimization theory, statistics and data analysis.
  • Experience with ontologies and related KR approaches in industry applications (relevant keywords include Knowledge Graph, Linked Data, semantic integration)
  • Experience in handling state-of-the-art Computer Vision or NLP models.

Don’t hesitate to get in touch with us. We are pleased to get in touch with you and are looking forward to read your filled contact form.

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