Staff Engineer: Director Oncology Data, Science Platforms (m/f/d)

As Staff Engineer for Applied AI Engineering, you will participate in the technical execution and development behind AstraZeneca's most critical AI innovations as well as working towards our mission of pushing the boundaries of science to deliver life-changing medicines for patients. Your mandate spans developing multiple AI work-streams sponsored by Executive Leadership and focusing on coding agentic solutions and generative AI.

Reporting into the Director of Software Engineering, you will be a senior individual contributor delivering prototypes, minimum viable products (MVPs), and technical implementations to transform scientific research.

This is a senior individual contributor position focused on hands-on technical execution. You will be directly coding, architecting and building AI solutions that drive AstraZeneca's digital transformation initiatives.

Role Responsibilities

• Design, code and build high-quality AI proof of concepts, MVPs and use cases through hands-on development

• Translate high-level business opportunities into concrete technical implementation plans, partnering closely with business, product, and architecture leaders

• Define and enforce engineering best practices across AI prototyping, including architecture patterns, documentation standards, quality gates, and reproducibility criteria

• Act as a key contributor to the Applied AI strategy and AstraZeneca's broader agentic AI framework, including involvement in shared component development and internal tooling

Basic Qualifications

• Minimum of a Master’s degree, preferably in STEM (Science, Technology, Engineering, Mathematics) or related field, required

• 10+ years of experience (after academic degree) in software engineering, or applied AI roles, with 5+ years in a senior individual contributor position

• Master Full-Stack technologies, such as Python, JavaScript / TypeScript, Cloud (preferably AWS), SQL, Unix.

• Experience delivering solutions to complex problems, from concept to pilot in enterprise environments, preferably across multiple business domains

• Technical foundation in machine learning, and AI

Preferred Qualifications

• Expertise in LLM-based applications, multi-modal, multi-agent systems, or agent orchestration frameworks

• Experience in highly regulated industries (e.g. pharma, healthcare, finance)

• Familiarity with benchmarking, interpretability, and risk mitigation techniques for applied AI

• Expertise in one or more areas such as NLP, time series, LLMs, generative models, and traditional machine learning (nice to have)

Benefits

• Individual development opportunities and a focus on lifelong learning.

• A diverse, inclusive and unbiased work environment.

• Trust, appreciation and space for co-creation.

• Wellbeing and Mobility Benefits