We are looking for the best
The lead Machine Learning Engineer at 42dot spearheads research and development efforts focused on machine learning algorithms for ensuring safe autonomous driving. They tackle complex challenges that haven't been easily resolved through traditional machine learning methods, aiming to achieve autonomous driving capabilities that rival human-level naturalness. Moreover, they foster collaboration with multiple teams across 42dot to leverage machine learning effectively.
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
Please refer to the videos from KCCV 2022 and UMOS Day 2021 for insights into 42dot Autonomous Driving, our autonomous driving AI software.
Please upload all submission files in PDF format.
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