Intern - AI/ML

Role Overview

The primary focus of this internship is on literature survey, data collection, benchmarking, and performance evaluation of AI/ML solutions across various automotive use cases. The intern will conduct systematic studies of state-of-the-art approaches, identify relevant datasets, perform comparative evaluations, and provide recommendations based on technical analysis.

The scope may include a broad range of automotive AI domains such as perception, driver monitoring, sensor fusion, scene understanding, prediction,anomaly detection, and other emerging automotive AI applications.

Key Responsibilities

• Conduct literature reviews of recent AI/ML, deep learning, and automotive AI research publications.
• Identify, collect, organize, and evaluate datasets relevant to different automotive use cases.
• Study and compare state-of-the-art models, architectures, and methodologies from academia and industry.
• Reproduce selected experiments and perform benchmarking studies where applicable.
• Evaluate approaches using relevant performance metrics such as accuracy, precision, recall, F1-score, mAP, latency, robustness, memory footprint, and computational efficiency.
• Analyze strengths, limitations, failure modes, and applicability of different solutions.
• Prepare technical reports, presentations, benchmarking summaries, and recommendations.

Required Skills

• Strong fundamentals in Machine Learning, Deep Learning, and Artificial Intelligence.
• Good programming skills in Python and familiarity with PyTorch or TensorFlow.
• Understanding of model evaluation methodologies and performance metrics.
• Ability to read, analyze, and interpret research papers and technical documentation.
• Strong analytical thinking, problem-solving, and technical documentation skills.

Preferred Qualifications

• Exposure to automotive AI, ADAS, autonomous driving, perception, or intelligent transportation systems.
• Experience working with automotive datasets such as KITTI, nuScenes, Waymo, BDD100K, or similar datasets.
• Familiarity with computer vision, multimodal AI, transformer-based architectures, foundation models.
• Knowledge of model optimization, ONNX, TensorRT, edge deployment, or embedded AI systems.
• Basic understanding of automotive sensors, including cameras, radar, LiDAR, and sensor-fusion approaches.