Machine Learning Engineer (Auto Labeling)
Mountain View, United States
42dotFull-time
We are looking for the best
At 42dot, our Machine Learning Engineers conduct research and development on machine learning algorithms to ensure safe autonomous driving. We address complex challenges that aren't easily solved through conventional means, aiming to achieve human-level natural autonomous driving. Additionally, we collaborate with various teams across 42dot to leverage machine learning effectively.
Responsibilities
- Dataset and Evaluation: We focus on curating high-quality datasets tailored to autonomous driving scenarios and designing robust evaluation metrics to accurately assess algorithm performance.
- Active Learning: Our team explores techniques for efficiently selecting and labeling informative data points, minimizing labeling efforts while enhancing model performance.
- Network Architecture Search: We investigate methods for autonomously discovering optimal neural network architectures, specifically tailored for label generation from sensor and video data in autonomous driving contexts.
- Transfer/Low-shot/Long-tail Learning: Our efforts include 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.
- Efficient Learning and Inference: We optimize learning algorithms and inference processes to ensure resource-efficient utilization, crucial for real-time deployment in autonomous driving systems.
- Privacy: Our team prioritizes 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.
Qualifications
- Minimum of 5 years of relevant experience
- Master's or Ph.D. degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field relevant to machine learning, or equivalent experience
- Strong background in Linear Algebra, Probability, Signal Processing, and machine learning concepts
- Proficient programming skills in languages such as C/C++, Python, and others
Preferred Qualifications
- Experience in development related to autonomous driving and robotics, including Object Detection, Semantic Segmentation, Depth Estimation, and Transformer-based models.
- Experience in building and utilizing automated learning pipeline systems.
- Track record of publications or contributions in relevant fields, such as CVPR, ICCV, ECCV, NeurIPS, AAAI, etc.
- Enjoyment in discovering and solving new problems in the field.
Interview Process
- Application Review - Coding Test - 1st Interview - 2nd Interview - Offer
- The process may vary by position and is subject to change
- Schedule and results will be communicated via the email provided in your application
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.
※ Please review the following information before applying.
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