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Key job responsibilities
- Modeling development with individual modalities, synergistically combining them, and scaling modeling methods to learn with large models and datasets
- Working on hardware-aware efficient model architecture, training objective and curriculum design
- Large-scale distributed training and developing accelerated optimization methods
- Training throughput and Model FLOPs Utilization (MFU) optimization
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- 1+ years of building models for business application experience
- Experience programming in Java, C++, Python or related language
- PhD in Computer Vision, Computer Science, Electrical Engineering, Mathematics or related field
- Experience with ML systems, ML Compilers or large-scale distributed training with PyTorch
- Experience with popular deep learning frameworks, including PyTorch
- Experience with learning multimodal LLMs and Gen AI in Computer Vision, both in the image and video domains
- Experience with patents or publications at top-tier peer-reviewed conferences or journalsPursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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