Responsibilities
- Lead the curation of high-quality datasets specific to autonomous driving scenarios and design robust evaluation metrics for accurate algorithm assessment
- Provide leadership in exploring techniques for efficient selection and labeling of informative data points, minimizing labeling efforts while enhancing model performance
- Lead investigations into methods for autonomously discovering optimal neural network architectures and tailor architectures for label generation from sensor and video data
- Provide strategic direction in developing strategies to leverage knowledge from related tasks or domains and address scenarios with limited labeled data and class distribution imbalances
- Provide leadership in optimizing learning algorithms and inference processes, ensuring resource-efficient utilization for real-time deployment
- Take a lead role in prioritizing the development of privacy-preserving techniques and provide direction in handling sensitive data while maintaining high-performance label generation
Qualifications
- 10+ years of hands-on experience (Ph.D. candidates nearing graduation may apply)
- Master's or Ph.D. degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field, or equivalent expertise
- Profound knowledge in Linear Algebra, Probability, Signal Processing, and machine learning concepts
- Advanced programming proficiency in languages like C/C++, Python, and others
Preferred Qualifications
- Extensive development experience in autonomous driving and robotics, including Object Detection, Semantic Segmentation, Depth Estimation, and Transformer-based models
- Proven track record in building and utilizing automated learning pipelines
- Strong academic background with publications or contributions in prestigious conferences and journals like CVPR, ICCV, ECCV, NeurIPS, AAAI, etc
- Passion for discovering and solving new problems in the field
0 applies
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