
ML Engineer for Parking, Planning and Prediction
This role requires employees to be onsite five days a week.
Role:
· Design and implement state-of-the-art machine learning algorithms for data-driven parking behavior prediction and decision-making.
· Optimize models for real-time inference, ensuring high performance and generalization across diverse environments.
· Design scalable, optimized behavior and motion planning software architectures for Level 2/3/4 autonomous parking systems
· Design, prototype, test, and release cutting-edge planning software stacks for Lucid production programs
· Work closely with other teams to ensure a seamless and robust implementation and deployment of motion planning products for autonomous parking systems
· Support the production verification and validation of the motion planning algorithms using prototype vehicles and pre-production vehicles
Required Qualifications:
· Master's degree Computer Science, Robotics, ML or equivalent experience
· In-depth knowledge of designing and training large scale models and multi-modal applications
· Professional experience solving problems using machine learning tools and integrating the solution in a production system
· Proficiency in C++, Python, PyTorch, TensorRT, and hands-on SWE design skills
· Comfortable with fundamentals of physics, probability, and statistics
· Excellent communication skills and willingness to learn
Preferred Qualifications:
· Ph.D. degree preferably in ML, robotics, or related field
· Experience in AV planning and behavior prediction
· Publications on top-tier conferences like CVPR/ICCV/ECCV/ICLR/ICML/NeurIPS/ICLR/AAAI/IJCV
· Experience developing real-time systems
· Hands-on experience testing complex AV systems on real-world platforms
· Familiarity with ROS2, QNX, git, CI/CD
Base Pay Range (Annual)
$128,800 - $177,100 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