Research Engineer – Machine Learning for Video Compression – Machine Learning Engineering
| Company | Qualcomm |
|---|---|
| Location | San Diego, CA, USA |
| Salary | $140800 – $211200 |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s, PhD |
| Experience Level | Mid Level, Senior |
Requirements
- Knowledge of Neural Networks based data compression, and the theory, algorithms, and techniques used in video and image coding.
- Experience in video compression standards, such as VVC/H.266 or HEVC/H.265, is a significant benefit.
- Excellent programming skills including Python and C/C++ combined with knowledge of at least one machine learning framework such as PyTorch.
- Strong written and verbal English communication skills, great work ethic, and ability to work in a team environment to accomplish common goals.
- PhD degree with relevant work experience or publications in the areas of video compression, video/image processing algorithms, or machine learning.
- PhD or Masters degree in Electrical Engineering, Computer Science, Physics, Mathematics, or similar fields.
- 1+ years of experience with programming language such as C, C++, MATLAB, etc.
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
- Master’s degree in Computer Science, Engineering, Information Systems, or related field and 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
Responsibilities
- Contribute to the conception, development, implementation, and optimization of new Neural Networks based algorithms allowing improved video compression.
- Represent Qualcomm in the related standardization forums: JVET, MPEG Video, and ITU-T/VCEG.
- Document and present new algorithms and implementations in various forms, including standards contributions, patent applications, conference papers and presentations, and journal publications, etc.
Preferred Qualifications
- Track record of successful research accomplishments demonstrated through published papers at leading conferences, and/or patent applications in the field of applications of Machine Learning to image or video compression.