FAQs
What is the duration of the internship?
The internship is a paid position lasting 12 weeks during the summer of 2025.
Where is the internship based?
The internship is based in Los Altos, CA, and is an in-office role.
What are the main responsibilities of the intern?
The intern will define project scope, conduct research on real-time neural network inference, implement and optimize machine learning models, evaluate techniques for model efficiency, and analyze experimental results.
What qualifications are required for the internship?
Candidates should be currently pursuing a Ph.D. in relevant fields such as machine learning, AI, or computer science, have a publication background in related venues, and possess a strong foundation in machine learning and deep learning.
What skills are preferred for this internship?
Preferred skills include hands-on experience with deep learning frameworks like PyTorch, knowledge of generative models, experience with image-to-image processing techniques, and proficiency in C++ and/or Python in a Linux environment.
Is there an expectation regarding publications?
Yes, applicants are encouraged to have a publication background and should include a link to their Google Scholar profile with a full list of publications when submitting their CV.
What is the pay range for this internship?
The pay range for this position is expected to be between $45 and $65 per hour, depending on various individualized factors such as location, knowledge, skills, and experience.
Are there opportunities for professional development during the internship?
Yes, the internship provides an opportunity to apply knowledge to novel research questions and work towards potential publications with professional researchers.
Does TRI offer benefits as part of the internship?
Yes, TRI offers a generous benefits package that includes vacation and sick time.
What is the company’s stance on diversity and inclusion?
TRI is committed to fostering a diverse and inclusive community and believes that diversity makes the organization stronger. They provide Equal Employment Opportunity for all applicants.