Sr. Multimodal Model Training and Inference Optimization Engineer

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.