Principal Research AI Engineer - Center for AI

Publiceringsdatum: 2026-01-15

Skulle du vilja jobba inom data/it? Positionen som civilingenjör, systemutveckling hos Astrazeneca AB är öppen och väntar på en lämplig kandidat - kan det vara du? Astrazeneca AB söker en medarbetare på heltid som vill arbeta tills vidare. Kan det vara du?

Platsen där jobbet finns ledigt är i Göteborg. Astrazeneca AB behöver fler anställda som stödjer arbetet inom civilingenjör, systemutveckling. Kolla in deras lediga tjänst för att se om du har vad som krävs. Astrazeneca AB letar efter erfaren civilingenjör, systemutveckling som kan ansluta till deras team. Sista ansökningsdag är torsdag den tjugonionde januari 2026.

Jobbannons

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