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