Machine Learning Engineer (Auto Labeling)

42dot is a company focused on developing autonomous driving technology and machine learning algorithms for safe autonomous driving.
Pangyo-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, South Korea
Machine Learning
Hybrid
5+ years of experience
AI · Automotive

Description For Machine Learning Engineer (Auto Labeling)

42dot is seeking a Machine Learning Engineer specializing in Auto Labeling for autonomous driving. The role involves developing machine learning algorithms for safe autonomous driving, solving complex problems, and implementing human-like autonomous driving. The engineer will collaborate with various teams at 42dot that utilize machine learning.

Key responsibilities include:

  1. Developing algorithms and automated systems for generating labels using various sensor and video data collected during autonomous driving.
  2. Curating high-quality datasets for autonomous driving scenarios and designing robust evaluation metrics.
  3. Researching active learning techniques to efficiently select and label high-value data points.
  4. Exploring methods for automatically discovering optimal neural network architectures for label generation.
  5. Developing strategies for transfer learning, low-shot learning, and long-tail learning to address limited labeled data and class imbalance issues.
  6. Optimizing learning algorithms and inference processes for efficient resource utilization in real-time autonomous driving systems.
  7. Prioritizing the development of privacy-preserving technologies for high-performance label generation while complying with privacy regulations.

The ideal candidate will have:

  • 5+ years of practical experience (Ph.D. candidates nearing graduation are welcome to apply)
  • Master's or Ph.D. in Computer Science, Electrical Engineering, Mathematics, Statistics, or Machine Learning-related fields
  • Strong knowledge of linear algebra, probability, signal processing, and machine learning
  • Proficient programming skills in C/C++ and Python

Preferred qualifications include experience in autonomous driving and robotics development, building and utilizing automated learning pipeline systems, and a track record of publications in relevant conferences (CVPR, ICCV, ECCV, NeurIPS, AAAI, etc.).

The interview process includes document screening, coding test, first interview, second interview, compensation negotiation, and final acceptance. The process may vary depending on the position and circumstances.

Join 42dot to work on cutting-edge autonomous driving technology and contribute to the future of transportation!

Last updated 7 days ago

Responsibilities For Machine Learning Engineer (Auto Labeling)

  • Develop algorithms and automated systems for generating labels using sensor and video data from autonomous driving
  • Curate high-quality datasets for autonomous driving scenarios and design robust evaluation metrics
  • Research active learning techniques for efficient data point selection and labeling
  • Explore methods for automatically discovering optimal neural network architectures
  • Develop strategies for transfer learning, low-shot learning, and long-tail learning
  • Optimize learning algorithms and inference processes for efficient resource utilization
  • Develop privacy-preserving technologies for high-performance label generation

Requirements For Machine Learning Engineer (Auto Labeling)

Python
  • 5+ years of practical experience (Ph.D. candidates nearing graduation are welcome to apply)
  • Master's or Ph.D. in Computer Science, Electrical Engineering, Mathematics, Statistics, or Machine Learning-related fields
  • Strong knowledge of linear algebra, probability, signal processing, and machine learning
  • Proficient programming skills in C/C++ and Python

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