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Lead Multi-Modal AI Research Engineer
2 months ago
Company Overview: Moonhub is at the forefront of innovation in the Healthtech sector, focusing on advanced AI solutions that enhance medical practices.
Position Overview: We are seeking a dynamic Founding Multi-Modal Research Engineer with a robust background in vision-language models, video comprehension, and visual question answering. The ideal candidate will possess a deep understanding of cutting-edge transformer architectures, especially in their pre-training and fine-tuning on extensive datasets. Experience in constructing foundational AI models is a considerable asset.
Key Responsibilities:
- Innovative Research: Spearhead groundbreaking research in multi-modal AI, focusing on vision-language models and sophisticated video comprehension techniques.
- Model Development: Drive innovation with transformer models, concentrating on their pre-training and fine-tuning on large-scale datasets.
- Foundation Model Creation: Engineer foundational AI models that are versatile across various tasks within our platform.
- Data Management: Enhance the utilization of extensive datasets for training to ensure the development of robust, scalable, and efficient models.
- Team Collaboration: Collaborate closely with cross-functional teams, including data scientists, radiologists, and product managers, to align research with strategic business goals.
- Thought Leadership: Contribute to the AI community through scholarly publications, patents, and presentations at esteemed conferences.
- Continuous Advancement: Stay informed on AI trends and incorporate leading-edge research into ongoing projects.
Qualifications:
- Experience: Minimum of 5 years in AI research, focusing on language or vision models, with at least 1 year dedicated to vision-language integration or video comprehension.
- Expertise: In-depth knowledge of transformer models, with practical experience in pre-training and fine-tuning large models.
- Specialization: Strong proficiency in video comprehension, video question answering, and visual question answering.
- Foundation Models: Proven experience in developing foundational models is highly advantageous.
- Technical Skills: Proficient in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Research Excellence: A strong portfolio of high-quality research, evidenced by publications in leading conferences or journals.
- Problem-Solving: Exceptional analytical abilities, with a knack for creatively tackling complex research challenges.
- Communication: Excellent verbal and written communication skills, capable of articulating complex concepts to diverse audiences.