• 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 environment 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.
• Robust 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.