Machine Learning EngineerNew
The opportunity
At Lucid, we are creating exceptional mobility experiences through innovation to drive the world forward. Built on Lucid’s proprietary technology and software-defined vehicle architecture, our award-winning vehicles bring our “Compromise Nothing™” approach to the global automotive market.
What you'll do
Design and deploy production-scale auto-labeling solutions for 2D and 3D: object detection, semantic and panoptic segmentation, lane and road-structure extraction, multi-object tracking, occupancy, scene understanding, and traffic-participant behavior.
Develop automated label validation, confidence scoring, consistency checks, and quality-monitoring frameworks.
Build active-learning and human-in-the-loop workflows that improve label: quality while reducing manual annotation effort and turnaround time.
Create teacher-student and model-assisted labeling workflows for large-scale camera and LiDAR datasets.
Research, train, and evaluate camera-based and LiDAR-based detection,: segmentation, tracking, depth-estimation, and occupancy models.
Develop multimodal fusion models combining camera, LiDAR, radar, GNSS/IMU, and map context.
What they're looking for
- Improve temporal consistency, calibration robustness, and long-tail: performance across diverse driving environments and edge cases.
- Build end-to-end ML workflows from raw sensor ingestion and preprocessing: through annotation, quality control, and dataset delivery.
- Develop 3D Gaussian Splatting (3DGS) and related reconstruction pipelines for: large-scale driving scenes collected from fleet vehicles.
- Use reconstructed scenes for label propagation, synthetic annotation: generation, annotation validation, rare-case augmentation, and simulation.
