Sr. Machine Learning Optimization Engineer - Autonomous Driving

Leading the future in luxury electric and mobility At Lucid, we set out to introduce the most captivating, luxury electric vehicles that elevate the human experience and transcend the perceived limitations of space, performance, and intelligence. Vehicles that are intuitive, liberating, and designed for the future of mobility.   We plan to lead in this new era of luxury electric by returning to the fundamentals of great design – where every decision we make is in service of the individual and environment. Because when you are no longer bound by convention, you are free to define your own experience.   Come work alongside some of the most accomplished minds in the industry. Beyond providing competitive salaries, we’re providing a community for innovators who want to make an immediate and significant impact. If you are driven to create a better, more sustainable future, then this is the right place for you.

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. 

  At Lucid, we don’t just welcome diversity - we celebrate it! Lucid Motors is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, national or ethnic origin, age, religion, disability, sexual orientation, gender, gender identity and expression, marital status, and any other characteristic protected under applicable State or Federal laws and regulations.Salary Range: The compensation range for this position is specific to the locations listed below and is the range Lucid reason