Key Responsibilities 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.

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.

Solid 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.

Required

SkillsAi/ml,C++,Python,pytorch,ADAS,Sensor Fusion,Cuda,ML ModelsSupported SkillsC++,AI/ML, Agentic AI, Python,PyTorch,autonomous driving, robotics, or real-time perception systems&&

📌 AI/ML, Robotics and Computer Vision with ADAS(C++/Python) Experts (Bengaluru)
🏢 KPIT
📍 Bengaluru