About Lucid
At Lucid, we are redefining the luxury electric vehicle experience, combining cutting-edge technology with exceptional design to deliver intuitive, safe, and exhilarating mobility. Join our team and help shape the future of autonomous driving.
The Role
We are seeking a Staff Software Engineer to lead the integration and deployment of advanced perception models into Lucid’s production ADAS and autonomous driving systems. This role focuses on productizing ML models, ensuring robust performance on automotive-grade hardware, and building scalable pipelines for deployment and validation. You will collaborate with ML researchers, perception engineers, and hardware teams to deliver high-performance, safety-compliant solutions.
Key Responsibilities
- Model Integration & Productization
- Deploy and integrate perception models (camera, LiDAR) into Lucid’s centralized software stack.
- Transition experimental components into production-ready modules with robust scheduling and diagnostics.
- Performance Optimization
- Optimize inference pipelines using CUDA, TensorRT, and mixed-precision techniques for real-time performance.
- Implement multithreaded scheduling and containerized deployments for automotive platforms.
- Pipeline Development & Automation
- Build CI/CD pipelines for nightly deployments, HIL verification, and KPI reporting.
- Automate data recording, evaluation frameworks, and regression testing.
- Cross-Functional Collaboration
- Work closely with ML researchers, software engineers, and OEM partners to ensure seamless integration.
- Support SDK development and customer-facing deliverables.
- System Diagnostics & Monitoring
- Implement runtime performance metrics, logging, and error reporting for perception stack reliability.
Required Qualifications
- BS/MS in Computer Science, Electrical Engineering, or related field.
- 7+ years of experience in software engineering for perception or autonomous systems.
- Strong proficiency in C++ and Python, with experience in ROS1/2, OpenCV, and GPU acceleration.
- Hands-on experience with ML frameworks (PyTorch) and inference optimization (TensorRT, CUDA).
- Expertise in containerization (Docker), CI/CD, and automated deployment pipelines.
- Proven ability to lead technical projects and collaborate across teams.
Preferred Qualifications
- Experience with automotive perception systems and ADAS software stacks.
- Familiarity with automotive safety standards (ISO 26262, ASPICE).
- Knowledge of multithreaded scheduling and real-time performance tuning.
- Background in SDK development and automotive customer integration.