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AstraZeneca
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Senior AI Engineer
ICT
Company shared salary
NA
Market rate
40,000 GBP–70,000 GBP/mo (480,000 GBP–840,000 GBP/yr)
Based on similar roles (title + domain + location).
About the Company
About AstraZeneca At AstraZeneca, we put patients first and strive to meet their unmet needs worldwide. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality. If you are swift to action, confident to lead, willing to collaborate, and curious about what science can do, then you're our kind of person. Beijing Site info The Beijing AI Center is a new strategic investment by AstraZeneca to accelerate drug discovery through AI. The center brings together AI researchers, computational scientists, and engineers to apply foundation models, agentic AI, and large-scale scientific computing to real R&D problems. Situated in one of the world's most dynamic AI talent markets, the center operates at the intersection of AI and biologics discovery, computational chemistry, and data-driven drug development.
About the Role
AstraZeneca's Beijing AI Center brings together Discovery science, AI platforms, and infrastructure to accelerate drug discovery through artificial intelligence. The Senior AI Engineer is one of the first engineering hires in Beijing AI Center, responsible for making the center's GPU investment productive for science teams. Together with with global AZ colleagues you will shape the AI engineering standards, and compute orchestration policies that enable AI scientists to train foundation models, run fine-tuning experiments, and scale inference workloads. You are the necessary bridge between IT's hardware infrastructure and Discovery's scientific workloads.
Responsibilities
- ● Distributed Training
- ● Design and validate multi-node multi-GPU training templates
- ● Build operational runbooks covering common failure modes, checkpointing, recovery
- ● Establish baseline performance benchmarks (throughput, step time, scaling efficiency)
- ● Optimize data loading pipelines to eliminate I/O bottlenecks in distributed settings together with IT AI Engineering Standards
- ● Provide training method standards: naming conventions, experiment configuration and tracking, model registry, reproducibility criteria
- ● Support to setup scheduling policies in close collaboration with IT: GPU quota rules, priority tiers, job templates for the center's Kubernetes/Run:AI platform Fine-tuning and Optimization
- ● Build reusable fine-tuning pipeline templates for models and scientific AI workloads
- ● Optimize training code for NVIDIA GPU to boost efficiency and throughput
- ● Collaborate with NVIDIA on hardware-specific optimizations Cross-Organizational Coordination
- ● Participate in coordination meetings across different AZ departments
- ● Align with wider AZ AI Engineering to define standards for scientific teams
- ● What You Bring
- ● + years expertise with production-grade model training and inference using PyTorch
- ● + years of experience with standard software development practices and tools, including Jira, Git, and the software development lifecycle (SDLC)
- ● Proficient in setting AI/ML engineering standards for teams (not just personal projects)
- ● Hands on experience with GPU workload optimization and multi-node trainings
- ● Kubernetes job scheduling experience (Kubeflow, Slurm, Run:AI, or equivalent)
- ● Ability to work full-time in Beijing Preferred
- ● Knowledgeable in molecular simulation, protein folding, drug discovery, or protein structures
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