Machine Learning Engineer – Productization & Deployment; ADAS​/Autonomous Driving

About Lucid

At Lucid, we are redefining the luxury electric vehicle experience, combiningcutting-edge technology with exceptional design to deliver intuitive, safe, and exhilarating mobility. Join our team and help shape the future of autonomous driving.

We are seeking a Staff Software Engineer to lead the integration and deployment of advancedperceptionmodels 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,perceptionengineers, and hardware teams to deliver high-performance, safety-compliant solutions.

• Model Integration & Productization
• Deploy and integrateperceptionmodels (camera, LiDAR) into Lucid’s centralized software stack.
• Transition experimental components into production-ready modules with robust scheduling and diagnostics.
• Performance Optimization
• Optimizeinference pipelines using CUDA,Tensor
  
  RT, 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 forperceptionstack reliability.

• BS/MS in Computer Science, Electrical Engineering, or related field.
• 7+ years of experience in software engineering forperceptionor autonomous systems.
• Strongproficiencyin C++ and Python
  , with experience in ROS1/2, OpenCV, and GPU acceleration.
• Hands-on experience with ML frameworks (PyTorch) and inference optimization (Tensor
  
  RT, CUDA).
• Expertise in containerization (Docker), CI/CD, and automated deployment pipelines.
• Proven ability to lead technical projects and collaborate across teams.

• Experience with automotiveperceptionsystems 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 andautomotivecustomer integration.