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Lead Research Engineer in AI Innovation

2 months ago

California, United States Moonhub Full time

Company Overview: Moonhub is at the forefront of innovation in the Healthtech sector, focusing on transformative solutions that enhance radiology practices worldwide.

Position Overview: We are seeking a visionary Founding Research Engineer specializing in multi-modal AI technologies. The ideal candidate will possess a wealth of experience in vision-language models, video comprehension, and advanced question-answering systems. A robust understanding of cutting-edge transformer architectures, particularly in their pre-training and fine-tuning processes, is essential. Experience in constructing foundational AI models will be highly regarded.

Key Responsibilities:

  • Innovative Research: Spearhead groundbreaking research initiatives in multi-modal AI, with a focus on vision-language integration and sophisticated video analysis techniques.
  • Model Development: Drive innovation in transformer models, concentrating on their pre-training and fine-tuning using extensive datasets.
  • Foundation Model Creation: Design and develop foundational AI models that are versatile and applicable across various tasks within our platform.
  • Data Management: Enhance the utilization of large datasets for training purposes to ensure the development of robust, scalable, and efficient models.
  • Team Collaboration: Collaborate closely with interdisciplinary teams, including data scientists, radiologists, and product managers, to align research efforts with organizational goals.
  • Thought Leadership: Contribute to the AI research community through publications, patents, and presentations at leading conferences.
  • Continuous Advancement: Remain at the cutting edge of AI trends and incorporate the latest research findings into ongoing projects.

Qualifications:

  • Experience: A minimum of 5 years of research experience in AI, with a specialization in either language or vision models, and at least 1 year focused on vision-language integration or video comprehension.
  • Expertise: In-depth knowledge of transformer models, with practical experience in their pre-training and fine-tuning on large-scale datasets.
  • Specialization: Strong expertise in video comprehension, video question answering, and visual question answering.
  • Foundation Models: Proven experience in the development of foundational models is highly desirable.
  • Technical Skills: Proficiency in Python and familiarity with deep learning frameworks such as PyTorch or TensorFlow.
  • Research Excellence: A demonstrated history of high-quality research, evidenced by publications in top-tier conferences or journals.
  • Problem-Solving: Exceptional analytical skills, with the ability to creatively tackle complex research challenges.
  • Communication: Strong verbal and written communication skills, capable of articulating complex concepts to diverse audiences.