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 TikTok, 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.
• 3 years+ 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:

• Preferring candidates with publications in venues such as MLSys, NeurIPS, ICLR, or ICML etc.
• 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.