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.
- Positive 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.

📌 Intern - AI/ML (Pune)
🏢 Varroc
📍 Pune