Job Description

(2026 internship)

Changing the world and user experiences with learned world knowledge is what our team is about. We are looking for Research Interns to work with a diverse group of researchers to innovate and advance novel/SOTA new computer vision and generative AI solutions and algorithms for the next generation consumer applications, pushing forward the research and development of cutting-edge generative machine learning (ML), computer vision (CV), computer graphics (CG). The ideal candidate is a highly motivated and creative individual with broad knowledge in SOTA ML/CV/CG, and with demonstrated ability for new technology research and a proven record of influential publications in related fields. The candidate will play a significant role in developing novel algorithms, innovative system solutions, and open-source prototypes; generating high-value IPRs/patents; and publishing our latest findings in well-known conferences and journals. If you have a passion for solving challenging problems; gaining valuable experiences and knowledge in applied research; making significant impact on real-world applications of Machine Learning and Artificial Intelligence technologies, all in a flexible, highly collaborative, but fast paced team, then we’d love to talk to you.

Requirements

• Proven hands-on experiences with SOTA machine learning algorithms, especially with generative machine learning and/or fine tuning of LLMs/LVMs, and familiarity with ML implementation environments and platforms such as PyTorch and/or Tensorflow.
• Familiar with the ML algorithm and application development lifecycle, data acquisition & annotation, model design & implementation, algorithm testing and improvement.
• Deep knowledge and hands-on experience in computer vision and computer graphics is a plus.
• Strong programming skills in python or C / C++ for Windows and/or Linux is required.
• Experience in working with open-source software frameworks is a plus
• Good communication skills, both verbal and written
• Ph.D. or M.S. in Computer Science / Machine Learning / Statistics, or related technical disciplines.