Machine Learning Engineer Automotive Unit
Machine Learning Engineer Automotive Unit
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1 Vacancy
Job Description
ZF LIFETEC is one of the worlds leading suppliers of passive safety systems. As Simi Automotive a global innovation leader for motion capture software and complete systems for recording and analyzing human movements and a subsidiary of ZF LIFETEC we contribute our experience with camerabased systems to developing solutions for vehicle interior monitoring and occupant safety. We are working on adaptive restraint systems with our colleagues at ZF LIFETEC.
Tasks
We are seeking a highly skilled and motivated ML Engineer to join our AI team. As an ML Engineer you will work on a variety of exciting projects in the domain of vehicle interior monitoring and production quality monitoring. You will collaborate with the software developers and data engineers to design train deploy and evaluate
stateoftheart ML algorithms for our automotive demonstrators.
Responsibilities
- Train and evaluate stateoftheart deep neural networks for tasks including imagebased classification detection segmentation and pose estimation.
- Implement wellstructured and documented code for training pipeline data pipeline qualitative analysis and performance evaluations.
- Deploy models on embedded/restricted hardware.
- Data sampling and analysis for specific ML tasks.
- Analyzing and documenting the quantitative and qualitative performance of ML models.
- Providing valuable feedback and recommendations for the data collection and labeling for ML tasks.
- Analyzing model performance in reallife demonstrations in vehicles.
Requirements
Required Qualifications
- Masters degree in computer science or similar (Please send your detailed diploma).
- Experience in Machine learning projects during (student) job internship or thesis
- Profound knowledge in machine learning deep learning and computer vision.
- Profound programming skills in Python and its packages including PyTorch Tensorflow openCV etc.
- Knowledge of neural network model porting to embedded systems.
Preferred Qualifications
- Research paper implementation and personal GitHub page
- Knowledge of stateoftheart neural network model architectures.
- Data distribution understanding and its correlation to training.
- Experience with synthetic data generation techniques e.g diffusion models variational autoencoders GANs etc.
- Experience with model optimization techniques for embedded hardware e.g quantization pruning etc.
We offer the opportunity to take on responsibility and make a difference flat hierarchies and flexible working hours. In addition to working in our new modern office in Unterschleiheim it is also possible to work from home part of the time. A highly motivated team of software developers and machine learning experts who work together with the target to make driving even safer is looking forward to meeting you!
Employment Type
Full Time
