PHARMAFull-time
Principal Scientist, Generative AI
By Admin · Data Science, Analytics & AI
BEIJING YIZHUANG (CN)ONSITEPOSTED: 8/17/2026
PHARMAFull-time
HIRING ORGANIZATIONASTRAZENECAPharmaceuticals / Biopharmaceuticals / Healthcare
ROLE DEFINITION & RESPONSIBILITIES
Role Overview
Principal Scientist, Generative AI is a hands-on, forward-deployed-engineer-style role within AstraZeneca's Predictive AI and Data team. The team provides AI and bioinformatics solutions to scientists across pre-clinical and clinical drug development, with the goal of accelerating drug discovery and development by combining company data with advanced AI technologies. The role will work directly with scientific customers to configure and implement generative AI solutions, including Large Language Models, foundation models, and agentic AI workflows, demonstrate business value, and help transition successful prototypes into robust production solutions.
Core Responsibilities
Collaborate directly with scientists across the drug development pipeline to understand scientific and operational challenges.
Build platforms and AI-enabled solutions that underpin scientific research and accelerate existing workflows.
Develop generative AI prototypes to demonstrate measurable value in routine scientific processes.
Design and deploy machine learning models for large-scale analysis of clinical transcriptomics, proteomics, and Cell Painting data.
Develop and integrate agentic AI workflows that connect multiple analysis steps across bioinformatics and machine learning workflows.
Work with machine vision, transcriptomics, language foundation models, and other advanced AI technologies.
Calculate ROI and expected business impact for AI initiatives using stakeholder and end-user assumptions and information.
Provide strategic direction for organizational generative AI adoption and collaborate with senior leadership to define and implement the AI strategy.
Identify opportunities for generative AI adoption and drive measurable business impact.
Champion a production-first approach so exploratory research can be transitioned into scalable production environments.
Ensure appropriate infrastructure, platforms, tooling, and deployment capabilities are available for AI solutions.
Build and maintain effective stakeholder relationships to maximize utilization and value from information resources and services.
Communicate AI results clearly and objectively, including uncertainties and limitations, to guide solution development and decision-making.
Continuously improve AstraZeneca's machine learning development environments, platforms, and tooling.
Collaborate across China, India, Europe, and the US East Coast time zones with AI research teams.
Communicate AI model requirements and evaluate available technical solutions across global teams.
Work closely with Cyber Security, Data Privacy, governance, and compliance functions to secure computing environments while preserving end-user productivity.
Generative AI & Machine Learning Expertise
Large Language Models and other foundation models.
Agentic AI workflows and agentic frameworks.
Prompt engineering.
Retrieval-Augmented Generation (RAG).
LLM fine-tuning.
Deep learning, natural language processing, computer vision, and reinforcement learning.
Machine vision and multimodal AI.
Machine learning model design, development, deployment, and productionization.
AI-driven automation of scientific processes.
Large-scale analysis of high-dimensional unstructured datasets.
Machine Vision & Scientific AI
Familiarity with CNNs, Vision Transformers, diffusion models, self-supervised learning, and multimodal training.
Relevant models include ResNet, UNet, DINO, CLIP, and Stable Diffusion.
Experience or familiarity with transcriptomics and Cell Painting foundation models such as Geneformer, scGPT, and scFoundation is desirable.
Ability to apply AI across transcriptomics, proteomics, Cell Painting, and bioinformatics workflows.
Software, Cloud & MLOps
Advanced Python programming skills.
Experience with TensorFlow and/or PyTorch.
Strong software development and machine learning deployment principles.
GitHub and version-control workflows.
CI/CD pipelines.
DevOps and MLOps best practices.
AWS or a comparable cloud environment.
Kubernetes and/or container-based application deployment.
Experience building production-ready AI infrastructure and scalable machine learning environments.
Familiarity with LangChain, AutoGen, and LlamaIndex is desirable.
Leadership & Strategic Impact
Provide strategic direction for generative AI adoption across a large global organization.
Lead successful AI projects from prototype through production.
Evaluate AI opportunities based on business value and ROI.
Partner with senior leadership to shape generative AI strategy.
Manage complex stakeholder relationships across scientific, technical, governance, and business functions.
Lead and coordinate work across multiple geographic regions and time zones.
Communicate complex AI concepts and technical findings to non-technical partners.
Promote responsible AI and ethical AI practices.
Collaboration & Global Working
Work closely with scientists across AstraZeneca's research and development organization.
Collaborate with AI research teams in China, India, Europe, and the US East Coast.
Partner with data science, software development, governance, Cyber Security, and Data Privacy teams.
Work effectively in complex, globally distributed environments.
Education & Essential Experience
Bachelor's degree, Master's degree, or equivalent experience in mathematics, computer science, engineering, physics, statistics, computational sciences, or a related discipline.
Advanced programming capability in Python and experience with modern AI libraries/frameworks such as TensorFlow and PyTorch.
Proven AI and machine learning experience across areas such as deep learning, NLP, computer vision, and reinforcement learning.
Experience implementing prompt engineering, RAG, and LLM fine-tuning.
Demonstrated experience implementing generative AI workflows using LLMs, foundation models, or agentic frameworks.
Experience automating existing processes or enabling new workflows with generative AI.
Experience manipulating and analyzing large, high-dimensional, unstructured datasets and translating findings into recommended actions.
Experience designing agentic AI workflows and planning strategically for enterprise AI needs.
Strong software development and ML deployment knowledge.
Familiarity with modern machine vision architectures and multimodal/self-supervised training.
GitHub, CI/CD, DevOps, and MLOps experience.
AWS or similar cloud experience.
Kubernetes and/or container deployment experience.
Excellent communication and presentation capabilities.
Strong leadership and project management skills.
Knowledge of AI ethics and responsible AI practices.
Desirable Skills & Experience
Life sciences, healthcare, or pharmaceutical industry experience.
Experience working in a complex global organization.
DevOps and MLOps experience supporting automation strategies.
Experience with LangChain, AutoGen, or LlamaIndex.
Agile delivery experience.
Product-focused or platform-focused development experience.
Knowledge of transcriptomics and Cell Painting foundation models including Geneformer, scGPT, and scFoundation.
Publications in leading AI conferences or journals, particularly in pharmaceutical research, such as NeurIPS, ICML, Nature Machine Intelligence, Nature Communications, or NEJM AI.
Company & Culture
AstraZeneca promotes diversity, equality of opportunity, and an inclusive workplace.
The company seeks perspectives from a wide range of backgrounds and encourages applications from qualified candidates.
Employment decisions are made in accordance with applicable non-discrimination, work authorization, and employment eligibility requirements.
REQUIRED VERIFIED SKILLS
Python
TensorFlow
PyTorch
Artificial Intelligence
Machine Learning
Deep Learning
Natural Language Processing
Computer Vision
Reinforcement Learning
Generative AI
Large Language Models
Foundation Models
Agentic AI
Prompt Engineering
Retrieval-Augmented Generation
LLM Fine-Tuning
Machine Learning Deployment
High-Dimensional Data Analysis
Unstructured Data Analysis
Machine Vision
CNNs
Vision Transformers
Diffusion Models
Self-Supervised Learning
Multimodal Training
GitHub
CI/CD
DevOps
MLOps
AWS
Kubernetes
Container Deployment
Software Development
AI Ethics
Responsible AI
COMPENSATION & REWARDS
Salary: Competitive / Disclosed upon candidate shortlisting
ORGANIZATION CONTEXT & CULTURE
AstraZeneca is a global, science-led biopharmaceutical company focused on the discovery, development, and commercialisation of prescription medicines. The company operates in various therapeutic areas, including Oncology, Cardiovascular, Renal & Metabolism, and Respiratory & Immunology. AstraZeneca is committed to pushing the boundaries of science to deliver life-changing medicines and improve patient outcomes worldwide.
TEAM SIZE: 89,000+ employees
INDUSTRY: Pharmaceuticals / Biopharmaceuticals / Healthcare
RECOMMENDED TRAINING
1/5
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