This is an in-office role based in Barcelona, ES, with a requirement to work a minimum of three days per week on-site. Remote or travel flexibility is not available.
Are you ready to build agentic AI that reshapes how clinical trials are designed, run, and learned from—ultimately helping patients receive effective medicines sooner? In this role, you will transform high-value clinical workflows with systems that reason over documents and data, use enterprise tools, and expose their evidence so clinicians can trust and adopt them.
You will join a hands-on clinical AI group at the heart of our end-to-end Enterprise AI engine, working closely with clinical and R&D partners to move fast from prototype to production. You will write and test code, run disciplined experiments, and turn insights from direct user feedback into robust capabilities used across multiple studies and therapy areas. How would you apply your LLM engineering and experimentation skills to high-stakes, real-world decisions?
Accountabilities
- Agentic AI Development: Design and deliver LLM-driven, agentic workflows for trial planning, conduct, monitoring, review, and reporting that improve speed, quality, and evidence transparency.
- Production-Grade Engineering: Write production-quality Python and SQL; build services, data pipelines, retrieval systems, tool integrations, structured-output components, and automated tests to meet reliability, latency, and cost targets.
- Evidence and Safety by Design: Integrate structured and unstructured clinical sources with clear provenance, access controls, and data contracts; implement verification, citations, uncertainty handling, and human-in-the-loop review so systems fail safely.
- Experimentation and Evaluation: Build reproducible experiments and evaluation datasets; select meaningful metrics; perform error analysis; compare models, retrieval strategies, and orchestration patterns; translate findings into concrete model, prompt, tool, data, or workflow improvements with release evidence.
- Collaboration and Adoption: Co-create solutions with CRAs, clinical scientists, medical monitors, and operations teams; iterate through rapid prototyping and user feedback; contribute to technical design reviews, code reviews, documentation, risk assessments, and reusable components adopted across studies and functions.
- Thought Leadership and Learning: Track advances in agentic AI, clinical NLP, multimodal models, evaluation, and responsible AI; reproduce promising methods; present results clearly to specialist and non-specialist audiences; contribute to internal standards and external scientific engagement where appropriate.
Essential Skills / Experience
- PhD in Computer Science, Machine Learning, Biomedical Informatics, Computational Biology, Statistics, or a related quantitative field; or a master’s degree with equivalent applied research and engineering experience.
- Strong hands-on programming skills in Python and working knowledge of SQL, software testing, version control, APIs, and reproducible development practices.
- 1 to 3 years of post-PhD (or master’s-level equivalent) experience building applied ML, NLP, generative-AI, or agentic systems beyond notebooks or demonstrations.
- Understanding of modern language-model techniques, retrieval-augmented generation, embeddings, tool use, structured generation, and evaluation. - Ability to design controlled experiments, select meaningful metrics, perform error analysis, and communicate uncertainty.
- Experience working with complex, heterogeneous, or imperfect data and tracing outputs back to their sources.
- Curiosity about clinical development and the ability to learn domain workflows through literature, data, and direct collaboration with subject-matter experts.
- Clear written and verbal communication and a collaborative approach to working with product, engineering, clinical, and quality colleagues.
- Commitment to reproducibility, responsible AI, patient privacy, and the higher evidentiary standard required for clinical applications.
Desirable Skills/Experience:
- Experience with clinical NLP, biomedical language models, multimodal clinical data, trial protocols, EHR, EDC, CTMS, eTMF, safety data, or medical-monitoring workflows.
- Experience with agent frameworks, workflow engines, cloud infrastructure, containers, CI/CD, model serving, observability, or MLOps/LLMOps.
- Familiarity with knowledge graphs, ontologies, terminology systems, document intelligence, or entity and relation extraction.
- Understanding of GCP and GxP, and what reproducibility and evidence standards mean for code and models used in regulated clinical settings. Experience partnering directly with clinicians, clinical scientists, trial-operations teams, or other domain experts.
- Peer-reviewed publications, open-source contributions, patents, or evidence of deploying AI systems used by real customers.
Why AstraZeneca?
Here, you will help build the digital backbone of R&D, pairing cutting-edge AI with rich clinical data, diagnostics, and real-world insights to create solutions that go beyond medicines. You will work side by side with clinicians, product leaders, engineers, and data experts who move quickly, value rigor, and expect evidence. We bring different disciplines together to challenge assumptions and scale what works, so your code can progress from experiment to trusted capability used across studies. We value kindness alongside ambition, and we back curiosity with the resources to learn new science, explore new technologies, and turn disciplined engineering into measurable patient impact.
#EAI
Date Posted
08-oct-2026
Closing Date
30-nov-2026
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.