Machine Learning Engineer (Auto Labeling)
Description For Machine Learning Engineer (Auto Labeling)
42dot is seeking a Machine Learning Engineer specializing in Auto Labeling for their autonomous driving technology team. This role focuses on developing advanced machine learning algorithms for safe autonomous driving, specifically in the area of automatic data labeling and processing. The position combines cutting-edge ML research with practical applications in autonomous vehicles, requiring expertise in computer vision, deep learning, and efficient algorithm development. The ideal candidate will work on creating automated labeling systems, optimizing neural network architectures, and implementing privacy-preserving machine learning solutions. Located in Pangyo's Software Dream Center, the role offers an opportunity to work with a leading autonomous vehicle technology company while contributing to the future of transportation. The position requires strong technical skills, research experience, and the ability to solve complex problems in autonomous driving.
Responsibilities For Machine Learning Engineer (Auto Labeling)
- Develop algorithms and automation systems to automatically generate labels using various sensor and video data collected during autonomous driving
- Dataset and evaluation: Curate high-quality datasets for autonomous driving scenarios and design robust evaluation metrics
- Active learning: Research techniques to efficiently select and label high-value data points while minimizing labeling work
- Network architecture search: Explore methods to automatically discover optimal neural network architectures
- Transfer/low-shot/long-tail learning: Develop strategies to handle limited label data and class imbalance problems
- Efficient learning and inference: Optimize learning algorithms and inference processes for real-time deployment
- Privacy: Develop privacy protection technologies while maintaining high-performance label generation
Requirements For Machine Learning Engineer (Auto Labeling)
- 5+ years of practical experience (PhD candidates may apply)
- Master's/PhD in Computer Science, Electronic Engineering, Mathematics, Statistics, or Machine Learning related field
- Expert knowledge in linear algebra, probability, signal processing, and machine learning
- Proficient programming skills (C/C++, Python, etc.)
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