Wir suchen einen Ingenieur für Maschinelles Lernen, der sich für innovative Themen des Maschinellen Sehens begeistert wie z.B. selbstüberwachtes Lernen, unüberwachte Domänenanpassung, semantische Segmentierung, 3D-Darstellungen, Transformator-Architekturen, Kalibrierung, etc.
Lesen Sie hier die vollständige Anzeige auf Englisch:
Our product, Maddox AI, is an AI-based visual quality control solution, which can automate manually performed quality inspection for manufacturing companies. Maddox AI is an asset-light SaaS solution, which addresses those visual inspection tasks that are still performed manually, as conventional (=rule-based) computer vision methods fail. In product development, we closely collaborate with leading AI researchers from the Cyber Valley. Prof. Dr. Matthias Bethge, Prof. Dr. Alexander Ecker and Dr. Wieland Brendel have been researching in the field of machine learning and computer vision for years and are part of our founding team.
Maddox AI is used by DAX-30 companies as well as by large medium-sized enterprises. Our team consists of scientists, former strategy consultants, mechanical engineers, and software developers. We know that Layer7's success is only made possible by our unique team. As we continue to grow, we want to convince the best and brightest minds of our mission to establish Maddox as the modern quality management platform.
We are looking for a machine learning engineer who is passionate about cutting-edge machine vision topics such as self-supervised learning, unsupervised domain adaptation, semantic segmentation, 3D representations, transformer architectures, calibration, etc.
- Research, prototype and develop state-of-the-art deep learning models together with other machine learning engineers to develop novel and innovative machine learning solutions that solve the manufacturing problems of our clients
- Work collaboratively and on eye-level with our ML co-founders and other colleagues from the machine learning and software engineering teams
- Create large, efficient, and automated data processing and machine learning pipelines that scale across many customers and applications and build accurate and robust end-to-end ML based inference pipelines
- Master’s degree in computer science or related field
- Strong machine learning and deep learning background
- Highly skilled in Python and one or more popular deep learning frameworks (PyTorch or TensorFlow) and relevant computer vision libraries
- Experience in full machine learning development and release lifecycle: data analysis, data preprocessing and pipeline, modelling, tuning, productization and debugging
- Experience in typical software development cycle for machine learning systems, i.e., going from notebooks to modular Python software packages
- Ability to clearly formulate problems, approaches to solutions, and results
- PhD in computer science, machine learning, or related field or relevant work experience in either academia or industry (strongly preferred)
- End-to-end experience of developing computer vision applications from scratch and continuously improving at production
- Experience with optimizing ML models for low latency and high-throughput models on-the-edge environments using tools like TensorRT or PyTorch’s pruning and quantization libs
Note that preferred qualifications are just that: preferred. None of us started out with all boxes ticked. If some of these points apply to you, we definitely want to talk.
We work in flat hierarchies, value direct communication, learn a lot as a team and make important decisions together. At Layer7 you can expect the following benefits:
- Independent work on projects in the field of artificial intelligence / Industry 4.0
- Flat hierarchies, a growth perspective and very good development opportunities
- A dynamic and motivated team with great colleagues (with experience from BCG, IBM, SAP, Cyber Valley, etc.)
- A competitive fixed salary, 30 vacation days and the opportunity to participate in the company's development through virtual shares (VSOP)
- The possibility to work flexibly in Berlin, Tübingen or remotely
- Regular team events
Please use following link to apply and fill out the application form. Otherwise, we cannot consider your application. Thanks!
If you have any further questions, please feel free to contact us at firstname.lastname@example.org.