Machine Learning Engineer (Auto Labeling)

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.

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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

Preferred Qualifications

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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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