Staff Machine Learning Engineer
Posted Updated
- We are looking for an independent, dedicated Staff Machine Learning Engineer to join the Visual Computing Group (VCG) and contribute to redefining the world of game play and live streaming via advanced R&D
- VCG’s mission is to design and deploy advanced neural networks and machine learning (ML) for game rendering and streaming systems of SIE that exceed the state-of-the-art in runtime efficiency, visual quality and latency
- Develop and improve core generative models for graphics and video processing for a variety of applications, such as live video streaming, game rendering, etc
- Research and prototyping of new methods for graphics or video enhancement, and data generation pipelines from generative models
- Collaborate with team members on drafting publications and patent submissions
- Participate and assist the team in core R&D work with other teams within Sony Interactive Entertainment
- Solid background in one or more of the following:
- Evidence of leading technical developments by several publications in top-tier conferences like CVPR, ECCV, ICCV, SIGGRAPH or similar
- Strong background in: TensorFlow or PyTorch; Python and packages/libraries related to computer vision or graphics; evidenced by the development of advanced applications in image/video/graphics processing or computer vision (minimum 7 years),
- 7+ years of experience in demonstrable research experience in industry or academia
- PhD or MSc in Computer Science, Electronic Engineering, Artificial Intelligence, Machine Learning, Computer Graphics or a related field, or equivalent skills evidenced by work experience in the specific domain of the post
- (iii) some experience in training, validation and evaluation of deep neural network models on large datasets, evidenced by experience in using Python libraries like HDF5 or similar
- (ii) design and test of advanced convolutional, recurrent, transformer-based, diffusion-based or other neural network architectures in a task-specific manner
- (i) neural network architectures, evidenced (for example) by knowledge of how to formulate and test advanced loss functions in neural network design
- Publications, e.g. in top-tier conferences and journals: IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, conferences like IEEE CVPR/ICCV/ECCV, NeurIPS, ICML, ICLR, or similar
- Some theoretical understanding of the graphics pipeline, including coordinate transformations between frames of reference, PBR materials, rasterization, lighting, and ray tracing
- Experience in image processing theory and methods, evidenced by the development of practical designs in this area
- Some experience with 3D engines such as Unreal or Unity; in particular, generation of datasets, extraction of G-buffers, motion vectors, etc