XAI Software Developer in Berlin bei Telecommunications Heinrich Hertz Institute

Future. Discover. Together.

The "Explainable Artificial Intelligence" (XAI) group focuses on the development of powerful methods, and the analysis of aspects of interpretability and explainability in machine learning. A core research of the group is the intersection of explainability, fairness and robustness of machine learning models. Become a part of our team and join us on our journey of research and innovation!

What you will do

• Development of the next generation of XAI algorithms in novel model architectures and expert domains

• Practical application

• Evaluation

What you bring to the table

• An excellent degree (Master of Science or equivalent) in Machine Learning or a related field

• A solid theoretical foundation and practical experience in machine learning

• Experience in training and evaluation of machine learning models on large high performance computing infrastructure

• A strong understanding of and practical experience with Deep Neural Networks for image- and time series data

• A strong understanding in Statistical Data Analysis

• Practical and theoretical experience with large scale Recommender System

• Practical and theoretical Experience in the field of Cognitive Science

  • In Particular, experience in data (pre)processing in the context of psycho-physiological experimentation

• A strong research potential and a proven track record of existing publications in relevant fields, including previous experience in presenting research at international conferences and workshops

• A firm grasp of the latest frameworks, back-ends and programming languages relevant for Deep Learning, especially Python 3 and PyTorch

  • Experience in C++ Programming is highly welcome

  • Experience in using Javascript for frontend development for visualizations is highly recommended

• Excellent programming skills and experience with Open Source software development

• Familiarity with collaborative tools such as Git

• The ability to work independently, as well as in a team, in a structured manner

• Excellent knowledge of the English language in spoken and written form

• High levels of skill and motivation for shaping a more transparent future of AI

• 100% on-site availability to promote creative collaboration among peers

What you can expect

• The ability to work on exciting and highly relevant research topics in the domain of XAI

• A highly motivated, international team and an excellent hardware and compute infrastructure

• Collaborations with international machine learning experts, and the participation in activities of these groups in international conferences, such as NeurIPS, CVPR, ICML, etc.

• The opportunity to contribute to cutting-edge XAI research in high-profile research projects

• Fascinating challenges in a scientific and entrepreneurial setting

• Attractive salary package according to TvöD with 30 vacation days, pension scheme (VBL), wealth creation benefits (VWL)

• Modern and excellently equipped workspace in a central location

• Great and cooperative working atmosphere in an international team

• Flexible working hours

• Support if you wish to do a doctorate

• Opportunities to work from home

• Extensive training-, health- and sports offers

• Various employee discounts and discounted BVG ticket

• Offers for families (parent-child office, emergency care, leisure activities, etc.)

The position is initially limited to 2 years. An extension is explicitly desired.

The weekly working time is 39 hours. This position is also available on a part-time basis. We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. Appointment, remuneration and social security benefits based on the public-sector collective wage agreement (TVöD). Additionally Fraunhofer may grant performance-based variable remuneration com