Machine Learning Engineer (End-to-End Driving)

We are looking for the best

At 42dot, our team of skilled Machine Learning Engineers is dedicated to pioneering cutting-edge solutions in Motion Planning algorithms. We specialize in researching and developing advanced algorithms for predicting future paths, essential for ensuring the safety of autonomous driving systems. Leveraging extensive datasets, we conduct in-depth analysis of driving patterns to design algorithms that enable smooth and natural control of autonomous vehicles. Our commitment lies in delivering innovative technologies that pave the way for the future of autonomous driving.

• Research and development of motion prediction algorithms for autonomous driving such as surrounding vehicles and pedestrians
• Exploring predictive algorithms, prototyping, and validating
• Modeling data-driven driving algorithms
• Deploying models to autonomous driving systems by optimizing and improving the performance of algorithms
• Work with Perception/Planning engineers to improve self-driving performance
• Development of evaluation indicators and systems to evaluate developed algorithms

• Minimum of 7 years of relevant experience
• A master's/doctor's degree or higher, or equivalent experience in computer science, electronics, or related fields
• Deep understanding and practical experience in learning self-driving systems and deep learning models
• Deep understanding of vehicle dynamics models
• Superior programming skills (C/C++, Python, etc.)
• mathematical reasoning ability related to probability

• Experience in research and development related to autonomous driving and robotics
• ROS1/ROS2 experience
• Experience deploying predictive models in real-world environments
• Inference optimization experience (TensorRT, CUDA programming, etc.)
• History of books/academic activities in related fields (CVPR, ICCV, ECCV, IROS, ICRA, etc.)

Please refer to the videos from KCCV 2022 and UMOS Day 2021 for insights into 42dot Autonomous Driving, our autonomous driving AI software.

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