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.

- Solid 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.

Required Skills

Ai/ml,C++,Python,pytorch,ADAS,Sensor Fusion,Cuda,ML Models

Supported Skills

C++,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