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. Minimum Qualifications: - M.S or PhD in Computer Science, Electrical Engineering, Artificial Intelligence, or a related field. - Experience in AI model training optimization. - Strong software engineering skills, including proficiency in Python, C++, and CUDA. - Strong proficiency in deep learning frameworks such as PyTorch, Megatron and Deepspeed. - Experience with distributed training techniques such as data parallelism, model parallelism, and pipeline parallelism. - Knowledge of transformers and diffusion models. Preferred Qualifications: - Candidates with publications at conferences such as MLSys, NeurIPS, ICLR, or ICML are preferred. - Strong communication and teamwork skills. - Self-motivated and strong problem-solving skills. - Ability to work collaboratively in multi-functional teams. - Experienced in implementing and optimizing complex and performance-critical systems.