What you will do:
- Research, prototype, implement, and improve Machine Learning solutions on meshed 3D models and other additive manufacturing data, using existing or novel Geometric Deep Learning methods or other applicable techniques, to improve Additive Manufacturing (“3D Printing”) and 3D Design processes.
- Counsel and guide other Research Engineers working on Machine Learning projects, e.g. on data science topics and in regard to choice of models, algorithms, process, hyperparameters, etc.
- Work together with experts in math, Python/C++ software engineering, additive manufacturing, and sometimes all of the above.
- Proactively collaborate with product managers, business developers, and other stakeholders to identify needs and solutions, to understand their requirements and to get the most value out of your deliverables.
- Stay on top of relevant academic and industrial breakthroughs, and proactively look for opportunities to apply AI in our domains.
- Solve global problems during coffee breaks.
Who you are:
- A Machine Learning and AI enthusiast.
- An intelligent and creative mind, allowing you to find out-of-the-box solutions for non-standard problems.
- Eager to learn: you acknowledge your non-omniscience, and are always looking to improve your skills and gain new insights and knowledge. You also like to keep track of new AI developments, which you are able to triage and present to your colleagues.
What you bring to the table:
- Theoretical and hands-on experience in data science, constructing and optimizing deep neural nets, proper validation, data preparation, hyperparameter tuning etc., preferably using Tensorflow, Python, and Numpy.
- Unbridled enthusiasm about machine learning. E.g. you were eager to find out how AlphaGo Zero worked, and think adversarial neural nets are either very cool, very scary, or very anything else (‘trivial’ would be a bold yet valid option).
- Knowledge of broader machine learning / AI concepts, and when to apply them. You can survive discussions on topics such as random forests, reinforcement learning, residual nets, different initialization methods and nonlinearities, etc.
- You could research and compare existing Geometric Deep Learning methods, see their limitations, and research new ones.
- You can constructively interact with other human beings, both in person and via electronic media, and present abstract ideas clearly and concisely to technical experts, end users, managers, and other sentient beings.
- You proactively take the necessary steps to move your projects forward.
- You can read code you wrote over six months ago – feeling only minor levels of shame – and understand what it does on sight.
- Full professional proficiency in English.
We offer an inspiring and challenging job with growth potential in an innovative market. You will be part of a dedicated team within a dynamic company that highly values openness, trust and team spirit.