Sr. Machine Learning Optimization Engineer - Autonomous Driving

The Role 

Lucid’s ADAS/Autonomous Driving division is seeking a highly skilled Machine Learning Optimization Engineer to enhance the efficiency of deep learning models for real-time inference. This role focuses on optimizing perception models for deployment on high-performance automotive hardware, leveraging advanced techniques such as quantization, pruning, and custom CUDA implementations. 

Responsibilities 

• Analyze ADAS/AD hardware to identify deep learning model optimization opportunities, working closely with cross-functional engineering teams. 

• Lead the technical roadmap for deep learning inference optimization, implementing techniques such as quantization, compression, and pruning for current and future hardware platforms. 

• Develop and integrate custom model optimizations using internal datasets and benchmarks, ensuring seamless deployment within existing training and inference pipelines. 

• Debug and enhance deep learning deployment pipelines, optimizing preprocessing and postprocessing code for target devices using CUDA kernels to minimize latency. 

• Conduct unit tests and validation to ensure the reliability, accuracy, and efficiency of optimized models. 

• Collaborate with perception, software, and hardware teams to ensure optimized models meet real-time performance requirements. 

Required Qualifications 

• Strong experience in CUDA kernel development and TensorRT plugin optimization for deep learning inference. 

• Proficiency in C/C++ programming, particularly for embedded systems and real-time applications. 

• Solid understanding of deep learning model architectures, with hands-on experience optimizing models for deployment. 

• Familiarity with automotive safety standards (e.g., ASPICE, ISO 26262) and their impact on software development. 

• Bachelor's degree in Computer Engineering, Electrical Engineering, Automotive Engineering, Mechanical Engineering, or a related field. 

• Minimum 3 years of professional experience or a Ph.D. for senior positions. 

• Advanced degrees preferred. 

Preferred Qualifications 

• Experience working with automotive sensors (e.g., Camera, Radar, Lidar) in ADAS/AD applications. 

• Familiarity with agile development methodologies and collaborative software development. 

• Experience in system integration, testing, and verification at both the component and vehicle levels. 
• Knowledge of Neural Architecture Search (NAS) techniques for optimizing deep learning model architectures.