Principal Machine Learning Engineer- Perception
Motional- Full Time
- Junior (1 to 2 years)
Candidates must hold a Ph.D. or Master's degree in Robotics, Machine Learning, Computer Science, Electrical Engineering, or a related field. Expertise in deep learning frameworks such as PyTorch or TensorFlow is mandatory, along with proficiency in OpenCV and TensorRT. Strong Python programming skills and experience with Pandas are required, as well as experience in deploying deep learning models on resource-constrained systems. Candidates should have a solid foundation in data science and traditional machine learning techniques, with experience in automated data annotation and training deep learning models for video data being highly desirable.
The Staff Deep Learning Engineer will drive the entire perception system development life cycle, from problem definition to deployment and ongoing improvement. They will actively contribute to the development of the perception system, develop robust computer vision algorithms, and design deep learning models for urban scene perception. Collaboration with cross-functional teams for seamless integration of perception models is essential, along with analyzing data to identify performance bottlenecks. The role also involves automating improvement cycles of deep learning models and effectively communicating technical findings to stakeholders.
Autonomous traffic management platform using AI
Hayden AI has created an autonomous traffic management platform that uses artificial intelligence to turn vehicles into mobile street sensors, collecting real-time data to improve urban mobility. This data helps transit agencies enforce dedicated bus lanes, enhancing rider safety and making public transportation more efficient. Unlike competitors, Hayden AI combines mobile sensors with citizen-driven data for a comprehensive view of traffic conditions. The company's goal is to make cities safer and more efficient through its patented technology.