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Research Engineer Intern - Perception, E2E Autonomous Driving

13 days ago

Internship

In-person · Santa Clara, CA, US... more

We are seeking a highly motivated Research Intern to contribute to the development of next-generation end-to-end autonomous driving (E2E-AD) models inspired by recent advancements such as UniAD, VAD, and multi-task learning approaches. This internship provides a unique opportunity to work on unified, perception-to-planning architectures that integrate vision, sensor fusion, and control in a data-driven manner. The intern will work closely with researchers and engineers to develop models that enable self-driving vehicles to perceive, predict, and plan efficiently in real-world environments.

Responsibilities:

Conduct research on end-to-end autonomous driving architectures, focusing on unified perception, prediction, and planning models.
Implement and optimize multi-task learning approaches for driving tasks, including object detection, motion prediction, and trajectory planning.
Work with sensor fusion techniques combining multi-modal inputs (e.g., camera, LiDAR, radar) to improve perception and decision-making.
Develop spatio-temporal and motion planning transformers for holistic driving scene understanding
Train, fine-tune, and evaluate models using large-scale autonomous driving datasets and internal Plus datasets
Utilize simulators to test and validate models in diverse driving scenarios.
Optimize real-time inference and deployment of driving models for efficient execution on edge devices.
Contribute to research publications and open-source implementations of E2E-AD models.

Required Skills:

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 processing techniques.
Strong analytical and problem-solving skills.

Preferred Skills:

Past experiences in projects involving design, training or fine-tuning of various autonomous driving related models
Familiarity with autonomous driving datasets (e.g., nuScenes, Waymo).
Hands-on experience with simulators like CARLA, AirSim, or equivalent.
Knowledge of robotics and motion planning algorithms is a plus.
Publication record in relevant venues (CVPR, ICLR, ICCV, ECCV, NeurIPS, AAAI, SIGGRAPH)