Principal Research AI Engineer – Center for AI

Lead the next wave of AI Innovation
Join AstraZenecas Center for AI and push the boundaries ofwhatspossible. We combinecutting-edgescience with advanced AI to accelerate discoveries and deliver life-changing medicines. Hereyoullwork with visionary minds explore bold ideas and shape the future of healthcare through technology. Innovation starts with you.

The role is to bridgecutting-edgeAI research and production engineering enabling scientific breakthroughs through robust scalable and sustainable research infrastructure. This position provides strategic technical leadership in building the platforms tools and practices that accelerate AI-driven scientific discovery while championing sustainability and engineering excellence across AstraZenecas R&D and Science functions.

Key Accountabilities

Research Engineering Leadership

• Provide technical vision and leadership for research engineering initiatives that enable scientists and researchers to rapidly experiment iterate and scale AI solutions from concept to production

• Design and implement research infrastructure frameworks and tooling that bridge the gap between exploratory research and enterprise deployment ensuring reproducibility scalability and compliance

• Mentor research engineers and collaborate closely with research scientists to establish best practices that maintainscientific rigor while accelerating the path from research to impact
  

Sustainable AI Research Infrastructure

• Lead the definition and implementation of sustainability strategies for AI research workloads establishing metricsmonitoringframeworks and optimization practices to minimize environmental impact without compromising scientific quality

• Architectefficient distributed computing solutions for large-scale model training and experimentation across cloud and on-premises infrastructure optimizing for both performance and energy efficiency

• Drive innovation in resourceutilization experiment tracking and computational efficiency to reduce costs and carbon footprint of research activities
  

Research-to-Production Excellence

• Own the research engineering standards and practices within AI for Science developing comprehensive documentation reusable components and automation that enable seamless transition from research prototypes to production systems

• Partner with research teams infrastructure groupsMLOpsteams and external collaborators to create robust pipelines that support the full research lifecycle from hypothesis to validated deployable solutions

• Accelerate scientific discovery by implementing DevOps GitOps and MLOps automation tailored to research workflows reducing friction and manual intervention while maintaining experimental flexibility

Innovation and Scientific Enablement

• Identify and evaluate emerging research engineering methodologies tools and technologies that enhance both scientific capability and operational sustainability

• Lead initiatives to improve reproducibility experiment management and collaboration across distributed research teams

• Foster a culture of innovation that balances scientificexploration with engineering discipline enabling researchers to focus on discovery while maintaining production-quality standards
  

Essential Skills and Experience

• PhD in Computer Science Engineering or a relevant scientific field orMastersdegree with significant experience in research engineering or applied research roles

• Demonstrated track record of building research infrastructure and tooling that has enabled scientific teams to achieve measurable impact

• Extensive experience establishing research engineering best practices including experiment tracking reproducibility frameworks and research-to-productionworkflows

• Expertisein automating research workflows and the machine learning lifecycle with focus on enabling rapid experimentation and iteration

• Advanced proficiency in Python and the scientific computing ecosystem (NumPyPandas SciPy etc.) with understanding of how researchers use these tools

• Expert-level experience withPyTorchor similar frameworks including implementing customarchitecturesandoptimizing trainingworkflows

• Demonstrable Experiencewith DDP FSDP and multi-node training.

• Strong background with modern DevOps practices including GitHub/GitLab CI/CD pipelines and infrastructure-as-code applied to research environments

• Experience with runningworkloadsas containers container orchestration cloudand on-prem workloads.

• Demonstrated ability to collaborate effectively with research scientists translating research needs into robust engineering solutions
  

Desirable Skills and Experience

• Experience in research engineering roles within academic pharmaceutical biotech or life sciences organizations

• Background driving sustainability initiatives in research computing including carbon footprint measurement and green computing practices

• Experience working in regulated industries with compliance requirements (GxP FDA EMA) and translating research outputs to compliant systems

• Knowledge of drug discovery computational biology or scientific research processes

• Contributions to open-source research tools scientific software or published work at the intersection of research and engineering

• Experience with container orchestration (Kubernetes) and research workflow tools (KubeflowMLflow Weights & Biases or similar)

• Hands-on experience with cloud platforms (AWS GCP or Azure) for research workloads and experimentation at scale

• Relevant cloud or ML engineering certifications

• Familiarity with research data management versioning and governance practices

Why AstraZeneca

At AstraZenecawerededicated to being an excellent Place to Work. Where you are empowered to push the boundaries of science and unleash your ambitious better place to make a differencetomedicinepatientsand society. An inclusive culture that champions diversity and collaboration andalwayscommitted to lifelong learninggrowthand an exciting journey to pioneer the future of healthcare.

When we put unexpected teams in the same room we unleash ambitious thinking with the power to encourage life-changing -person working gives us the platform we need to connect work at pace and challenge perceptions.

Thats why we work on average a minimum of three days per week from the office. But that doesnt mean were not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our outstanding and ambitious world.