Research Engineer Intern - Perception, Multi-Modal Occupancy Map Generation and Forecasting

Excited to contribute to safe autonomous driving? This internship at Plus offers a unique opportunity to learn and advance state-of-the-art research on high-assurance occupancy map generation for end-to-end L4 self-driving vehicles.

Using cutting-edge transformer-based architectures, you’ll explore recent advances in the field and develop a unified vision- and signal-based multi-frame occupancy map generation and forecasting model. Collaborating with experienced perception, prediction, and planning engineers and researchers at Plus, you’ll help design model architectures and build pipelines to train and evaluate models on large-scale datasets.

• Develop multi-modal occupancy map generation and forecasting models 
• Develop pipeline to generate data, train, evaluate, and optimize deep learning models
• Contribute to real-time deployment, simulation testing, and research publications

• Pursuing MS or PhD in CS, EE, mathematics, statistics or related field
• Thorough understanding of deep learning principles and familiarity with perception, prediction and planning models
• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Experience with computer vision and sensor-fusion techniques
• Strong analytical and problem-solving skills.

• Past experience in designing and training models on autonomous driving data
• Familiarity with publicly available autonomous driving benchmarks
• Publication record in relevant venues (e.g., CVPR, ICLR, ICCV, ECCV, NeurIPS)
• Knowledge of robotics and motion planning algorithms is a plus.
• Hands-on experience with simulators like CARLA and AirSim