Torc is seeking a Machine Learning Engineer to contribute to building next-generation BEV space models for autonomous trucks, aiming to transform how the world moves freight.
Requirements
- Strong understanding of computer-vision, and machine learning.
- Experience with training and validating machine learning models.
- Mastery of Python and experience with PyTorch.
- Experience working with machine learning pipelines, familiarity with distributed systems such as Ray, and a strong interest in scalable training and deployment workflows.
- Understanding of BEV space 3D scene modeling, and multimodal learning in autonomous systems.
Responsibilities
- Implement deep learning models for object detection, semantic segmentation, and voxel grid occupancy in BEV frameworks.
- Enhance perception systems to process multi-modal sensor data (camera, LiDAR, radar) effectively.
- Implement monocular and stereo depth estimation algorithms.
- Identify and interpret objects, lanes, obstacles, and weather conditions in the driving environment.
- Apply data science techniques to analyze model performance, understand data distributions, and identify corner cases.
- Integrate BEV representations into end-to-end planning and control pipelines.
- Development of model-specific conversion, deployment and integration pipelines.
Other
- Bachelor's degree in computer science, data science, artificial intelligence, or related field with 4+ years of professional experience or a master's degree with 2+ years of experience.
- Good technical communication skills, written and verbal.
- A positive team-player mindset.
- PhD in machine learning or data science.
