Research Scientist Intern – Computer Vision for Generative AI – PhD
| Company | Meta |
|---|---|
| Location | Seattle, WA, USA, Menlo Park, CA, USA, New York, NY, USA |
| Salary | $45.000086538628 – $65.152048369324 |
| Type | Internship |
| Degrees | PhD |
| Experience Level | Internship |
Requirements
- Currently has or is in the process of obtaining a Ph.D. degree in Computer Vision or Artificial Intelligence, or relevant technical field.
- Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment.
- Experience with Python or other related language.
- Experience building systems based on machine learning and/or deep learning methods.
Responsibilities
- Develop novel state-of-the-art computer vision algorithms and corresponding systems, leveraging various deep learning techniques.
- Based on the project, help analyze and improve efficiency, scalability, and stability of corresponding deployed algorithms.
- Perform research to advance the science and technology of intelligent machines.
- Perform research that enables learning the semantics of data (images, video, text, audio, and other modalities).
- Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
- Publish research results and contribute to research that can be applied to Meta product development.
Preferred Qualifications
- Intent to return to degree program after the completion of the internship/co-op.
- Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP or similar.
- Experience working and communicating cross functionally in a team environment.
- Experience in advancing AI techniques in computer vision, including core contributions to open source libraries and frameworks in computer vision.
- Experience solving analytical problems using quantitative approaches.
- Experience setting up ML experiments and analyze their results.
- Experience manipulating and analyzing complex, large scale, high-dimensionality data from varying sources.
- Experience in utilizing theoretical and empirical research to solve problems.
- Experience with deep learning frameworks.