Perception Machine Learning Engineer - ADAS
- Develop and optimize perception algorithms for autonomous driving systems using camera and LiDAR data.
- Design and implement cutting-edge deep learning algorithms for 2D/3D object detection, segmentation, tracking, and multi-task learning.
- Research and integrate BEV-based transformer models for perception tasks.
- Collaborate with cross-functional teams to ensure seamless integration and robust implementation.
- Test, release, and deploy perception algorithms into Lucid production programs.
- Support validation and verification of perception algorithms using prototype and pre-production vehicles.
- Propose innovative software algorithms to enhance future autonomous driving capabilities.
- Strong theoretical foundations and expertise in deep learning algorithms, including object detection, tracking, and segmentation.
- Proficient in Python with a focus on clean, efficient, and scalable software development.
- Comfortable working with large codebases and debugging complex machine learning models.
- Experience with PyTorch or other ML frameworks (e.g., TensorFlow, MXNet).
- Ability to design and construct evaluation pipelines to unit-test ML models under diverse conditions and environments.
- Excellent communication skills and a strong team player.
- Bachelor’s degree in Computer Engineering, Electrical Engineering, Automotive Engineering, Mechanical Engineering, or a related field.
- Minimum of 3 years of relevant work experience, or a Ph.D. in a related field for a senior position.
- Advanced degrees are preferred.
- Experience developing BEV transformer models for perception.
- Proficiency in C++ with experience writing efficient, maintainable code.
- Practical, hands-on approach to solving complex problems in autonomous driving.
- Experience in testing and validating perception systems in real-world conditions.
- Experience working in agile development teams.
- Expertise in component and system integration, testing, and verification at the system and vehicle levels.
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