
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.
Base Pay Range (Annual)
$134,400 - $184,800 USD
By Submitting your application, you understand and agree that your personal data will be processed in accordance with our Candidate Privacy Notice. If you are a California resident, please refer to our California Candidate Privacy Notice.
Apply for this job
*
indicates a required field