Seattle, Washington, United StatesFull TimeSenior202k–368k USDPosted Today
About the team
The Vision-Applied Research team focuses on applied research in Generative AI and CV/Multimodal Understanding, and delivering intelligent solutions to ByteDance products, e.g., TikTok, CapCut, and Lemon8, enabling users to make and share creative content in a much easier way. The team has research groups dedicated to generative models for content creation, image generation, video synthesis, intelligent image/video editing, and virtual humans.
We are seeking an experienced Multimodal Model Training and Inference Optimization Engineer with expertise in optimizing AI model training and inference, including distributed training/inference and acceleration. The ideal candidate will work at the cutting edge of AI efficiency, enhancing the performance, scalability, and deployment of large-scale generative AI models.
Responsibilities
- Optimize large model training pipelines to improve efficiency, speed, and scalability.
- Develop and improve distributed training strategies such as data parallelism, model parallelism, pipeline parallelism and communication to accelerate model training.
- Benchmark and profile deep learning models to identify performance bottlenecks and optimize computational resources.
The base salary range for this position in the selected city is $202160 - $368220 annually.
The Vision-Applied Research team focuses on applied research in Generative AI and CV/Multimodal Understanding, and delivering intelligent solutions to ByteDance products, e.g., TikTok, CapCut, and Lemon8, enabling users to make and share creative content in a much easier way. The team has research groups dedicated to generative models for content creation, image generation, video synthesis, intelligent image/video editing, and virtual humans.
We are seeking an experienced Multimodal Model Training and Inference Optimization Engineer with expertise in optimizing AI model training and inference, including distributed training/inference and acceleration. The ideal candidate will work at the cutting edge of AI efficiency, enhancing the performance, scalability, and deployment of large-scale generative AI models.
Responsibilities
- Optimize large model training pipelines to improve efficiency, speed, and scalability.
- Develop and improve distributed training strategies such as data parallelism, model parallelism, pipeline parallelism and communication to accelerate model training.
- Benchmark and profile deep learning models to identify performance bottlenecks and optimize computational resources.
The base salary range for this position in the selected city is $202160 - $368220 annually.