About the Role
Role details below.
Responsibilities
- Develop and train computer vision and deep learning models for road-lane detection using monocular and multimodal sensor data (camera, LiDAR, radar).
- Build 3D road surface and lane geometry models in BEV space and integrate them into Torc’s autonomy pipeline.
- Analyze model performance, identify corner cases, and improve robustness under diverse environmental and long-tail conditions.
- Develop and optimize large-scale data processing workflows, including annotation, pseudo-labeling, and data augmentation.
- Implement scalable training and evaluation pipelines for lane perception models.
- Own deployment-focused work to optimize models for real-time execution on automotive-grade hardware.
- Leverage SD and HD map priors to improve lane estimation accuracy and stability.
- Contribute to architectural discussions, model reviews, and system-level integration efforts.
