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北京市, 中国
Onsite
Predictive AI and Data team is responsible for providing AI and Bioinformatics solutions to the scientists across the spectrum of drug development and discovery in AstraZeneca (both pre-clinical and clinical stages). The primary aim is to find ways to accelerate the drug development process by leveraging existing company data in combination with the most cutting-edge AI approaches in day-to-day scientific work across the company.
Introduction to role
This hands-on role is similar to a forward deployed engineer (FDE) and will work directly with customers across the spectrum of drug development pipeline to configure and implement generative AI solutions (including Large Language Models, other Foundation Models and Agentic AI workflows) to demonstrate value to the business in accelerating existing scientific processes. You will then help build out the robust solution in production in collaboration with other data science and software developer teams. In particular, using agentic AI workflows to integrate different analysis steps across bioinformatic and machine learning workflows will be a major focus and will include working with machine vision, transcriptomics, and language foundation models.
- Collaborate with scientists from across the company to understand their challenges and work with them to build the platform that underpins their research.
- Build generative AI prototype solutions to demonstrate value in accelerating routine scientific processes.
- Take responsibility for designing, and deploying machine learning models for a large-scale analysis of clinical transcriptomics, proteomics, and cell painting data
- Calculate ROI and impact for AI projects obtaining necessary information and assumptions from stakeholders and future users
- Champion a “production first attitude” to ensure the necessary infrastructure and platforms are available to scale exploratory research to production.
- Build and manage effective relationships with stakeholders to ensure utilization and value of information resources and services. Clearly and objectively communicate results, as well as their associated uncertainties and limitations to shape solutions
- Be a part of a hard-working team, continuously improving AstraZeneca’s Machine Learning development environments, platforms, and tooling.
- Work effectively across several timezones with AI research teams in China, India, Europe and the US East Coast, communicating the requirements for the AI models and evaluating the available solutions
- Work closely and collaboratively with internal governance and compliance functions such as Cyber Security and Data Privacy to secure the computing environment without obstructing end-user productivity.
- Bachelor’s degree, or Master’s (or equivalent years of experience) in mathematics, computer science, engineering, physics, statistics, computational sciences or a related field.
- Experience working in a scientific field (including biology, biotech, pharmaceutical or environmental research) and applying Machine Learning or AI models in that context
- Advanced skills in programming languages such as Python, and experience with AI libraries and frameworks (e.g., TensorFlow, PyTorch).
- Proven experience in AI and machine learning, in areas such as deep learning, natural language processing, computer vision, and reinforcement learning.
- Experience in prompt engineering, implementing Retrieval-Augmented Generation (RAG), and LLM fine-tuning
- Demonstrated experience in implementing generative AI workflows (large language models, other foundation models or agentic frameworks) to automate existing process or enable new ones, ideally in the Pharma and/or Healthcare space
- Experience of manipulating and analysing large high dimensionality unstructured datasets, drawing conclusions, defining recommended actions, and reporting results across stakeholders
- Experience designing agentic AI workflows and an ability to plan strategically for the AI needs in a large organisation
- Strong knowledge of software development and machine learning deployment principles
- Familiarity with existing machine vision models: CNNs, vision transformers, diffusion models etc. for self-supervised and multimodal training (e.g., ResNet, UNet, DINO, CLIP, Stable Diffusion)
- Familiarity with GitHub, CI/CD pipeline, and best DevOps and MLOps practices
- Demonstrable experience working with AWS or a similar cloud environment
- Experience working with Kubernetes and/or container-based application deployments.
- Excellent communication and presentation skills, with the ability to convey complex AI concepts to non-technical partners.
- Strong leadership and project management skills, with a track record of leading successful AI projects
- Knowledge of AI ethics and responsible AI practices
- Experience in life sciences, healthcare, or pharmaceutical industry.
- Familiarity with ADMET, DMPK, population pharmacology modelling
- Experience in a complex global organization.
- Experience using DevOps and MLOps to enable automation strategies
- Experience with LLM frameworks (LangChain, AutoGen, LlamaIndex)
- Experience working in an Agile team with knowledge or experience of working in product or platform-focused delivery
- Familiarity with modern foundation models for transcriptomics or Cell Painting data (e.g., Geneformer, scGPT, scFoundation etc.)
- Track record of publications in top AI conferences or journals in pharmaceutical research (e.g., NeurIPS, ICML, Nature Machine Intelligence, Nature Communications, NEJM AI, etc.)
Date Posted
23-9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.