Torc is looking to solve the problem of developing software for automated trucks to transform how the world moves freight, specifically focusing on Road-Lane BEV and image space models.
Requirements
Scientific understanding of machine learning for 3D BEV space modeling, including the ability to apply state-of-the-art ML research and methods in production.
Applied understanding and hands-on expertise in lane and road geometry concepts, multi-camera calibration, and sensor projection.
Experience with understanding data distributions and analyzing long tail distributions
Mastery of Python and PyTorch, with the ability to transition research level code to production and deployment ready standards
PhD in machine learning or data science
Proficient in writing CUDA kernels and developing custom PyTorch operations.
Applied experience using Ray in an autonomous vehicle (AV) or related environment to scale machine learning workloads, including distributed training, large-scale experimentation, and hyperparameter tuning across multi-node and multi-GPU systems.
Responsibilities
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.
Design and implement deep learning models for Road Model inference in BEV frameworks.
Integrate BEV representations into end-to-end planning and control pipelines.
Develop efficient pipelines for large-scale data processing and annotation (pseudo-labeling) of sensor data (e.g., LiDAR point clouds, image frames).
Other
Bachelor’s degree in Computer Science, Software Engineering, or related field with 6+ years of professional applied MLE engineering experience in Autonomous Vehicle, Robotics or related industry.
Master’s degree in Computer Science, Software Engineering, or related field with 3+ years of professional applied MLE engineering experience in Autonomous Vehicle, Robotics or related industry.
Flexibility in schedule and generous paid vacation (available immediately after start date)
100% paid medical, dental, and vision premiums for full-time employees
