ML Engineer, II - Road & Lane

Meet the Team 

As a Machine Learning Engineer II – Road & Lane, you will help develop next‑generation models that estimate road surfaces, lane geometry, and lane topology within Torc’s autonomy stack. You will work closely with perception, mapping, and planning teams to deliver high‑quality, production‑ready lane perception models that enable safe and reliable autonomous trucking. 

What You'll Do 

• 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. 

What You'll Need to Succeed 

• Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related field with 4+ years of experience, or a Master’s with 2+ years. 

• Hands‑on experience developing ML models for perception tasks such as lane detection, road surface modeling, multi‑camera fusion, or related geometry estimation. 

• Strong understanding of camera calibration, multi‑sensor alignment, and projection between image and BEV spaces. 

• Proficiency in Python and PyTorch, with experience writing production‑quality machine learning code. 

• Experience training models on large datasets and using scalable compute environments. 

• Understanding of relevant ML architectures, such as CNNs, transformers, and BEV‑focused perception networks. 

• Ability to analyze model performance metrics, debug failure cases, and iterate effectively. 

• Ability to work cross‑functionally with autonomy, perception, and software engineering teams. 

Bonus Points 

• Experience working specifically on lane perception, BEV networks, or road topology estimation. 

• Experience with CUDA kernels or custom PyTorch operations. 

• Familiarity with SD maps, localization pipelines, or map‑based priors. 

• Experience with distributed training or large‑scale experimentation frameworks (e.g., Ray). 

• Publications in major ML/CV conferences (CVPR, ICCV, NeurIPS).