🛠 Tech Stack

💼 About This Role

You'll lead the development of deep learning models for road and lane perception, enabling Torc's autonomous trucks to interpret complex road geometry. You will define next-generation architectures like multi-camera BEV transformers and LiDAR-vision fusion models, driving measurable improvements in perception accuracy and scalability. This role focuses on model innovation, not downstream feature integration.

🎯 What You'll Do

• Own the model roadmap for Road Lane Detection from concept to production.
• Research and train advanced neural architectures for lane detection.
• Lead data strategy including labeling policies and active learning.
• Develop robust evaluation frameworks for lane geometry accuracy.

📋 Requirements

• 10+ years of experience in deep learning for perception or computer vision.
• Deep expertise in semantic segmentation, BEV modeling, or scene topology.
• Proficiency with Python and PyTorch/Lightning.
• Experience with distributed training and large-scale datasets.

✨ Nice to Have

• Experience with autonomous driving or ADAS systems.
• Familiarity with multi-modal fusion (camera, LiDAR, radar).
• Hands-on experience with BEV-based or topological prediction models.

🎁 Benefits & Perks

• 🏖️ Flexible schedule and generous paid vacation
• 💰 Competitive compensation with bonus and stock options
• 🏥 100% paid medical, dental, and vision premiums
• 🏦 401K plan with 6% employer match
• 🛡️ AD&D and Life Insurance