Job Responsibilities:
- Investigate and apply advanced Vision-Language Modeling techniques to autonomous driving challenges, including large-scale transformer-based architectures and multi-modal pre-training.
- Develop and refine vision-language models for tasks such as:
- Captioning and summarizing complex driving scenes
- Visual question answering about objects, actions, and intentions in traffic scenarios
- Aligning textual navigation instructions with visual perception for route planning
- Collaborate with other team members to integrate novel VLM-based solutions into existing autonomous driving frameworks.
- Evaluate and benchmark model performance on internal and public datasets, identifying gaps and proposing improvements.
- Document findings through internal research reports and contribute to publications in top-tier conferences if suitable results are achieved.
Minimum Qualifications:
- MS degree (currently pursuing PhD) in Computer Science, Electrical Engineering, Robotics, or a related field, with a strong focus on machine learning, computer vision, and/or natural language processing, etc.)
- 5+ years of relevant work experience
- Major in Computer Science, Electrical Engineering, Robotics, or a related field, with a strong focus on machine learning, computer vision, and/or natural language processing.
- Demonstrated experience in developing and training deep learning models, particularly in areas involving multi-modal inputs such as images, video, and text.
- Solid understanding of state-of-the-art vision and language models (e.g., CLIP, BLIP, VLM adaptations of ViT, LLM-integrated frameworks).
- Strong programming skills in Python and familiarity with deep learning libraries (e.g., PyTorch, TensorFlow).
Preferred Qualifications:
- Currently pursuing or recently graduated from a PhD program in Computer Science, Electrical Engineering, Robotics, or a closely related discipline
- Publication record in reputable AI/ML/CV/NLP conferences or journals
- Experience with Autonomous Driving algorithms and systems
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