Senior Ai Engineer

AstraZeneca

Beijing, China
Onsite in beijing
5+ years distributed deep learning training experience
Strong pytorch expertise with production-grade model training
Multi-node multi-gpu training templates (ddp, fsdp)
AstraZeneca is seeking a Senior AI Engineer for their new Beijing AI Center, focusing on leveraging AI to accelerate drug discovery. The role involves designing distributed training infrastructure and setting AI engineering standards while collaborating with various teams

Job Summary

  • The Senior AI Engineer is responsible for making the Beijing AI Center's GPU investment productive by designing distributed training infrastructure and AI engineering standards.
  • This role serves as the bridge between IT's hardware infrastructure and Discovery's scientific workloads to accelerate drug discovery through artificial intelligence.
  • AstraZeneca offers an inclusive culture that champions diversity and collaboration while empowering employees to push the boundaries of science.

Matching Summary

Match Score: 85

AstraZeneca is seeking a Senior AI Engineer for their new Beijing AI Center, focusing on leveraging AI to accelerate drug discovery. The role involves designing distributed training infrastructure and setting AI engineering standards while collaborating with various teams.

Skills & Requirements

Must-have

  • 5+ years distributed deep learning training experience
  • Strong PyTorch expertise with production-grade model training
  • Multi-node multi-GPU training templates (DDP, FSDP)
  • Kubernetes job scheduling experience (Run:AI, Kubeflow)
  • Hands-on Transformer optimization (FlashAttention)
  • Setting AI/ML engineering standards for teams

Nice-to-have

  • Parameter-efficient fine-tuning methods (QLoRA, LoRA)
  • Reinforcement learning training infrastructure
  • AWS China or Alibaba Cloud experience
  • NVIDIA H20 or H100-series GPU familiarity
  • Biopharma domain knowledge in drug discovery
  • Experience working across organizational boundaries

Key Requirements

  • 5+ years experience in distributed deep learning
  • Production-grade PyTorch model training skills
  • Multi-node cluster management proficiency
  • Kubernetes job scheduling platform experience
  • Transformer-based model optimization background

Work Rights

Not specified

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