Senior AI Engineer
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.
What You’ll Do
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
Required
- 5+ years expertise with production-grade model training and inference using PyTorch
- 5+ 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
- Experience in efficiently delivering high quality code through coding agents
- Knowledgeable about parameter-efficient fine-tuning methods
- AWS China or Alibaba Cloud experience
Desirable
- Hands-on experience improving transformer-based models
- Experience working across organizational boundaries serving multiple science groups
- Of experience in distributed deep learning training
- NVIDIA GPU familiarity (e.g. H20 or H100-series))
Working Environment
- Three-organization model: you work daily with Discovery scientists (biologics and computational chemistry team) and IT engineers
- Functional guidance from the global AI Engineering team for standards, methods, and career development
- Line management from the Head of Data & AI Platforms, Beijing
- Emphasis on practical delivery over academic novelty; this is an engineering role, not a research role
Why AstraZeneca?
At AstraZeneca we’re dedicated to being a Great Place to Work. Where you are empowered to push the boundaries of science and unleash your entrepreneurial spirit. There’s no better place to make a difference to medicine, patients and society. An inclusive culture that champions diversity and collaboration, and always committed to lifelong learning, growth and development. We’re on an exciting journey to pioneer the future of healthcare.
So, what’s next?
Are you already imagining yourself joining our team? Good, because we can’t wait to hear from you!
Where can I find out more?
Our Social Media,
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Date Posted
17-8月-2026Closing Date
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.