Principal Research AI Engineer – Center for AI

Lead the next wave of AI Innovation
Join AstraZeneca’s Center for AI and push the boundaries of what’s possible. We combine cutting-edge science with advanced AI to accelerate discoveries and deliver life-changing medicines. Here, you’ll work with visionary minds, explore bold ideas, and shape the future of healthcare through technology. Innovation starts with you.  

The role is to bridge cutting-edge AI 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 AstraZeneca's 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 maintain scientific 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 metrics, monitoring frameworks, and optimization practices to minimize environmental impact without compromising scientific quality 

• Architect efficient 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 resource utilization, 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 groups, MLOps teams, 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 scientific exploration 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, or Master's degree 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-production workflows 

• Expertise in 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 (NumPy, Pandas, SciPy, etc.), with understanding of how researchers use these tools 

• Expert-level experience with PyTorch or similar frameworks, including implementing custom architectures and optimizing training workflows 

• Demonstrable Experience with 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 running workloads as containers, container orchestration, cloud and 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 (Kubeflow, MLflow, 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 AstraZeneca we’re dedicated to being an excellent Place to Work. Where you are empowered to push the boundaries of science and unleash your ambitious spirit. There’s no better place to make a difference to medicine, patients and society. An inclusive culture that champions diversity and collaboration, and always committed to lifelong learning, growth and development. We’re on 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 medicines. In-person working gives us the platform we need to connect, work at pace, and challenge perceptions.

That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our outstanding and ambitious world.