Train and evaluate deep neural networks for tasks such as image-based classification, detection, segmentation, and pose estimation
Implement well-structured, documented code for training pipelines, data pipelines, qualitative analyses, and performance evaluations
Deploy models on embedded/constrained hardware
Perform data sampling and analysis for specific ML tasks
Analyze and document the quantitative and qualitative performance of ML models
Provide valuable feedback and recommendations for data collection and labeling for ML tasks
Analyze model performance in real-world demonstrations in vehicles

Master’s degree in Computer Science or a related field (please provide your detailed degree certificate)
Experience with machine learning projects during student jobs, internships, or thesis work
Solid knowledge of machine learning, deep learning, and computer vision
Strong programming skills in Python and its libraries, including PyTorch, TensorFlow, OpenCV, etc.
Experience porting neural network models to embedded systems
Nice to have: published research and a personal GitHub profile
Familiarity with state-of-the-art neural network architectures
Understanding of data distributions and their correlation to training
Experience with synthetic data generation techniques, e.g., diffusion models, variational autoencoders, GANs, etc.
Experience with optimization techniques for embedded hardware, e.g., quantization, pruning, etc.

Machine Learning Engineer – Automotive

Key skills