Frontier AI Research Principal - Cambridge, England, United Kingdom

We're building a connected, end-to-end Enterprise AI engine - uniting data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain. Success depends on being exceptional connectors: you'll actively leverage existing capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation. If you thrive in high-collaboration environments where your role is to turn complex, cross-functional problems into reusable, enterprise-wide capabilities - and where the measure of success is adoption and scale, not just innovation - you'll have the platform (and sponsorship) to make it real.

About AISI

AI Science & Innovation (AISI) sits at the centre of AstraZeneca's R&D AI transformation. Our remit is to build, buy and deliver the AI models and agents that change pipeline outcomes, across discovery, translational science, biomarkers and clinical development.

Introduction to role:

Are you ready to set the direction for frontier AI that accelerates scientific discovery and sharpens decisions for millions of patients? Can you turn cutting-edge ideas in agentic AI, LLMs, and computational modeling into reliable systems that change how we work end to end?

As Frontier AI Research Principal, you will define and deliver the roadmap for advanced AI across AstraZeneca. You will lead multidisciplinary teams to transform ambiguous scientific and business questions into scalable AI solutions, from target prioritization and evidence synthesis to operational optimization, deployed on cloud and high-performance computing. Your work will convert data into decisive action, enabling faster insights, smarter pipelines, and measurable patient impact.

Accountabilities:

- Strategic AI Leadership: Shape the vision and adoption of advanced analytics and AI capabilities, setting a focused roadmap that delivers measurable scientific and business outcomes.

- Team Leadership and Mentorship: Lead, grow, and inspire AI engineers and scientists; ensure bar-raising execution across complex, cross-functional AI programs.

- End-to-End Solution Delivery: Define, build, optimize, and deploy scalable AI/ML and computational modeling solutions across structured and unstructured data using classification, regression, clustering, deep learning, NLP, graph learning, recommendation systems, and more.

- Problem Framing and Portfolio Strategy: Translate complex scientific and business challenges into tractable analytical plans; prioritize a portfolio that moves from rapid experiments to productionized impact.

- Engineering Excellence and MLOps: Champion code quality, reproducibility, automation (CI/CD), observability and evaluation, with production deployment to cloud/HPC using Python, PyTorch, JAX, Hugging Face, MLflow, vLLM, Docker, and Kubernetes; employ agent frameworks such as LangGraph, OpenAI Agents SDK, Microsoft Agent Framework and Google ADK; use interoperability standards like Model Context Protocol (MCP) and Agent2Agent (A2A); instrument tracing and evaluation with OpenTelemetry and LangSmith.

- Stakeholder Partnership: Work closely with senior leaders, domain experts, and technology partners to align priorities and capture value from AI initiatives across science, operations, and the enterprise.

- Data Platforms and Analytical Rigor: Guide best practice for large-scale data engineering, feature generation, and model evaluation to support hypotheses, decisions, and regulated workflows.

- Ecosystem and Partnerships: Establish high-impact collaborations with internal teams and external partners, including academia and vendors, to accelerate delivery and innovation.

- Capability Uplift and Training: Provide expert consultancy and training to elevate AI, data science, and computational practices; foster a culture of continuous learning and digital innovation.

- Governance and Risk Management: Set clear deliverables, manage risks, and communicate outcomes, opportunities, and limitations with transparency.

- External Reputation: Publish, present, and collaborate externally to advance the state of the art and represent AstraZeneca’s leadership in AI.

Essential Skills/Experience:

- Master’s (or equivalent experience) in computer science, mathematics, engineering, statistics, Physics, or other quantitative discipline

- Significant experience in AI, machine learning, statistical modeling, and/or advanced data science

- Demonstrated leadership in managing and delivering cross-functional AI or data science projects and teams

- Track record of scientific and/or technical publications in AI or related fields

- Advanced understanding of AI engineering and/or computation (model optimization, infrastructure, automation) and AI science (modelling, data exploration, algorithm development)

- Excellent written and verbal communication, project management, and stakeholder management skills

- Proficiency in handling large, complex, and high-dimensionality datasets

Desirable Skills/Experience:

- PhD in a relevant quantitative discipline

- Experience in life sciences, healthcare, or pharmaceutical R&D

- Background in novel method development, model interpretation, indication or target discovery

- Work experience in a global, matrixed organization and with external academic or vendor partners

- Experience with budget management and influencing cross-functional priorities

- Expertise in pharma context may be required

Why AstraZeneca:

Join a science-led company where AI is central to delivering life-changing medicines. You will work in an environment that prizes curiosity and decisive action—unexpected teams in the same room unleashing bold thinking, supported by modern platforms, strong partnerships, and real-world datasets at scale. We value kindness alongside ambition, pairing rigorous standards with the psychological safety to experiment, learn fast, and iterate. Here your ideas move from whiteboard to production to patients, with the backing, tools, and collaboration needed to push boundaries and build what has not been built before.

Call to Action:

Seize this opportunity to shape how frontier AI advances medicine—bring your vision, and let’s build the future together.

#EAI

Date Posted

Closing Date