Design and develop end-to-end (perception-to-control) autonomous driving models
Build multi-modal architectures (camera, radar, LiDAR) using deep learning and transformer-based models.
Develop world foundation models for scene understanding, motion prediction, and structured workplace representation.
Implement self-supervised and large-scale representation learning pipelines.
Engineer robust training pipelines for large-scale real-world and simulated datasets.
Develop corner-case mining, adversarial robustness, and long-tail scenario handling strategies.
Optimize models for real-time automotive deployment (GPU/embedded platforms).
Integrate AI systems into safety-critical automotive frameworks.
Required Qualifications
4-5 years (minimum) of experience in AI/ML system development.
4+ years in autonomous driving, robotics, or real-time perception systems.
Strong expertise in Python and C++.
Deep experience with PyTorch or similar frameworks.
Hands-on experience with transformers, multi-modal learning, and large-scale model training.
Strong understanding of sensor fusion, state estimation, and vehicle dynamics.
Experience with distributed training and high-performance computing environments.
Experience deploying ML models in production systems.