Qualcomm Technologies, Inc. is seeking a Video Compression Research Engineer to develop algorithms, hardware architectures, and systems for state-of-the-art applications of classical and machine learning methods in video compression, video processing, point cloud coding and processing, AR/VR and computer vision use cases.
Requirements
- Knowledge of Neural Networks based data compression. Knowledge of 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 considered significant benefit.
- Track record of successful research accomplishments demonstrated through published papers, and/or patent applications preferably in the fields of application of Machine Learning to image or video compression.
- Excellent programming skills including Python and C/C++ combined with knowledge of at least one machine learning framework like PyTorch.
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 and journal publications, presentations, etc.
Other
- Masters or PhD degree with relevant work experience or publications in the areas of video compression, video/image processing algorithms, or machine learning.
- Strong written and verbal English communication skills, great work ethic, and ability to work in a team environment to accomplish common goals.
- Bachelor's degree in Engineering, Information Systems, Computer Science, Mathematics, Physics or related field and 8+ years of Software Engineering, Hardware Engineering, Systems Engineering, or related work experience.
- 10+ years of experience with programming language such as C, C++, MATLAB, etc.
- PhD or Masters degree in Electrical Engineering, Computer Science, Physics, Mathematics, or similar fields.
