AI Infrastructure Specialist

5 days ago

San Jose, California, United States beBee Careers Full time

Job Description:

We are seeking a highly skilled AI Infrastructure Specialist to join our team. As an expert in large-scale machine learning infrastructure, you will be responsible for designing and developing scalable and efficient AI systems.

Responsibilities:

  1. Architect and develop AI infrastructure to support large-scale diffusion models and multi-modal generative AI workloads.
  2. Optimize model training and inference using PyTorch, Triton, TensorRT, and distributed training libraries.
  3. Implement and optimize model using sequence parallelism, pipeline parallelism, and tensor parallelism to improve performance on high-throughput training clusters.
  4. Scale and productionize generative AI models, ensuring efficient deployment on heterogeneous hardware environments.
  5. Develop and integrate model distillation techniques to enhance the efficiency and performance of generative models.
  6. Design and maintain an automated model production pipeline for training/inference at scale, integrating distributed data processing frameworks.
  7. Enhance platform stability and efficiency by refining model orchestration, checkpointing, and retrieval strategies.

About the Role:

This is an exciting opportunity to join a team that is pushing the boundaries of AI research and development. As an AI Infrastructure Specialist, you will work closely with cross-functional teams to ensure seamless model iteration cycles and deployments.

Qualifications:

  • B.S., M.S., or Ph.D. in Computer Science, Electrical Engineering, or a related field.
  • 3+ years of hands-on experience in large-scale machine learning infrastructure and distributed AI model training.
  • Deep expertise in PyTorch, CUDA optimization, and ML frameworks.
  • Strong understanding of high-performance computing, low-latency inference, and GPU acceleration techniques.
  • Hands-on experience in scaling AI infrastructure, leveraging Kubernetes, Docker, Ray, and Triton inference servers.

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