Senior Machine Learning Engineer – Auto Labeling
| Company | 42dot |
|---|---|
| Location | Mountain View, CA, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Senior |
Requirements
- Master’s or Ph.D. in Computer Science, Electrical Engineering, Mathematics, Statistics, or a closely related field with relevance to machine learning.
- At least 7 years of hands-on experience in developing machine learning models and pipelines.
- Deep knowledge of Linear Algebra, Probability, Signal Processing, and machine learning fundamentals.
- Advanced programming skills in C/C++, Python, and related libraries/frameworks (e.g., PyTorch, TensorFlow).
Responsibilities
- Curating high-quality datasets tailored to autonomous driving scenarios and designing robust evaluation metrics to accurately assess algorithm performance.
- Exploring techniques for efficiently selecting and labeling informative data points, minimizing labeling efforts while enhancing model performance.
- Investigating methods for autonomously discovering optimal neural network architectures, specifically tailored for label generation from sensor and video data in autonomous driving contexts.
- Developing strategies to leverage knowledge from related tasks or domains, addressing scenarios with limited labeled data (low-shot learning), and handling class distribution imbalances (long-tail learning) commonly found in autonomous driving datasets.
- Optimizing learning algorithms and inference processes to ensure resource-efficient utilization, crucial for real-time deployment in autonomous driving systems.
- Prioritizing the development of privacy-preserving techniques, ensuring the handling of sensitive data while maintaining high-performance label generation, in compliance with privacy regulations and safeguarding user information.
Preferred Qualifications
- Extensive experience in autonomous driving or robotics applications, especially in Object Detection, Semantic Segmentation, Depth Estimation, and Transformer-based models.
- Expertise in designing and implementing automated learning pipelines for large-scale systems.
- Strong research background with publications in top-tier conferences/journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, AAAI).
- Proven ability to handle large-scale datasets and innovate solutions for rare and challenging edge cases.
- Passion for problem discovery and creative problem-solving in the field of autonomous systems.