About the job
We are seeking an experienced AI model 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.
Qualifications
Minimum
- Master’s or PhD in Computer Science, Electrical Engineering, Artificial Intelligence, or a related field.
- 2+ years of 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
No preferred qualifications listed.