Deep Learning Research Intern

Key Skills

Aartificial intelligence, Algorithms, Computer Vision (CV), Deep Learning, Effective communication skills, Machine learning techniques, Python Programming, PyTorch, TensorFlow

Job Description

About The Job

We are looking for a talented researcher in deep learning to join our ASID team in Santa Clara. We are passionate about applied deep learning research to solve computer vision problems in image restoration, model acceleration, 3D and Nerf, ViT, CLIP, VAE and Diffusion models, as well as feature embedding, contrastive learning and multi-modal learning etc.

Responsibility:

• Develop image restoration algorithms in model acceleration, 3D and Nerf, ViT, CLIP, VAE, and Diffusion models, as well as feature embedding, contrastive learning, and multi-modal learning etc.
• Develop demo prototypes for computer vision applications.
• Summarize the developed algorithms into academic papers and patent applications.

Qualifications:

• Ph.D. in Computer Science, Electrical Engineering, Artificial Intelligence, Mathematics, or close related field with relevant research experience.
• Excellent knowledge of theory and practice of machine learning and deep learning.
• Knowledge of applications areas such as language modal and computer vision.
• Strong experience with deep learning framework such as Tensorflow or PyTorch.
• Solid programming skills with Python.
• Proven track record of academic publication in top conferences and journals.
• Strong communication and interpersonal skills and the ability to work in a dynamic, global team.

Location: Santa Clara, CA

Hourly interns pay range: $18 to $59, depending on degree-seeking academic program (PhD, Master’s, Bachelor’s, etc.), years of relevant experience, year in school, geographic location, credentials, qualifications, and other job-related factors.

Housing allowance and relocation benefit might be provided to intern candidates who meet the qualifications. Additional details on the compensation package will be provided to candidates during the interview process.