Senior, ML Engineer - Road & Lane Detection
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About the Role
About the Company
At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.
A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight.
Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.
Meet the Team
Torc’s Model Development Organization is hiring a Senior ML engineer team who develops our next generation of Road-Lane BEV and image space models.
Torc's Autonomy Applications software utilizes cutting-edge deep learning techniques to perceive the vehicle's environment, predict the movements of other vehicles, and execute accurate driving decisions. We are actively seeking a highly experienced senior machine learning engineer to join our Road Lane perception team. This is an exceptional opportunity for you to have a significant impact on the future of the autonomous vehicle industry by leveraging AI.
As a Senior ML Engineer of the team, you are applying machine learning science in a production focused environment. You are using machine learning models in both a unimodal and multimodal context, to create a 3D representation of the road surface and lane geometry. Training, validation, data science, architectural design are your daily work. You are interested in understanding how your model performs in deployment, for what you collaborate closely with deployment focused teams. You mentor and guide more junior members of the team and are always interested in the newest trends in research, eager to translate scientific improvements into our production grade machine learning pipelines.
What You'll Do
Develop and Optimize Computer Vision Algorithms
- Training monocular and multimodal Road Model Detection models.
- Comprehending objects, lanes, obstacles, and weather conditions within the driving environment.
- Enhance perception systems to process multi-modal sensor data (camera, LiDAR, radar) effectively.
- Utilizing data science techniques to analyze model performance, data distributions, and identify corner cases.
Contribute to BEV Self-Driving Architectures
- Design and implement deep learning models for Road Model inference in BEV frameworks.
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