leader of AI Infrastructure

We are seeking a senior leader of AI Infrastructure to join Computational Innovation’s AI Accelerator. In this role, you will own the technical infrastructure strategy for the AI Accelerator, ensuring the unit has the compute, storage and platform infrastructure it needs to train, fine-tune and serve biomedical foundation models at scale. You will lead the architecture, standards and practices for the compute and platform substrate on which the other teams depend, working wider CI data teams and IT, as well as cloud providers, to ensure that our AI scientists and ML teams get the best from all available infrastructure. This is a technical leadership role that requires deep technical expertise and relationship building. You will be a senior member of the AI Enablement leadership team, contributing to the team’s overall direction alongside the Senior Staff Data Engineer and Senior Staff MLOps Engineer. This is a unique opportunity to be part of a critical strategic initiative for a pharmaceutical company that invests heavily in research and development to discover and develop innovative therapies that can improve and extend lives in areas of high unmet medical need., * Own, define and evolve a wholistic, multi-layer reference architecture spanning storage, compute, environments and platform stack, making appropriate use of vendor/integrator design patterns.

• Set the technical direction, strategy and roadmap for AI infrastructure, aligned to the various AI Enablement roadmaps and to portfolio/research priorities.
• Partner with Data Excellence to further development BI’s Trusted Research Environment (TRE), ensuring the TRE supports secure, compliant AI workloads on sensitive data and evolves to meet the AI Accelerator’s needs.
• In partnership with IT, shape the evolution of existing enterprise infrastructure to support large-scale AI and ensure best-value services while designing for portability.
• Establish ways of working, onboard and mentor other team members, and act as a senior escalation point for complex AI Infrastructure problems.

• PhD or MSc with equivalent experience in a STEM subject.
• Extensive experience as a senior staff-level infrastructure, platform or systems engineer for compute-intensive workloads, including setting architecture, standards and technical direction for a team or function.
• Deep expertise across the AI compute infrastructure stack: high-performance and GPU compute, storage and file systems, networking, container platforms and orchestration (e.g. Kubernetes), environment provisioning and infrastructure-as-code (e.g. Terraform).
• Significant experience with hybrid on-prem/cloud architectures, including right-sizing, cost optimisation and designing for portability to avoid lock-in.
• Expertise in providing infrastructure that enables large-scale AI workloads, including distributed training at scale.
• Understanding of security, access control, resilience/continuity and systems-operation rigour (ITIL or equivalent service management).
• Strong collaboration and influencing skills across technical and non-technical stakeholders; ability to explain complex technical concepts clearly and to mediate competing requirements.
• Experience mentoring or technically leading engineers and setting engineering principles.

AI ACCELERATOR Most diseases are still poorly understood at a biological level. Despite decades of research, the causal mechanisms driving many conditions remain unclear, limiting our ability to identify the right targets, design the right interventions and bring the right medicines to patients. The AI Accelerator exists to change that. Based in London and sitting within Computational Innovation (@computationalinnovation), a global organisation spanning computational biology, human genetics, data excellence and AI, the Accelerator’s mission is to build production-quality AI capabilities that deepen our understanding of disease biology and increase probability of success. We do this by applying neural-based methods across the biomedical data landscape to integrate heterogeneous, multimodal data sources, infer biological relationships and embed causal thinking into what we build. The goal is not just to predict but to explain and understand why disease occurs. It could be electronic health records and medical imaging to support patient segmentation. It could be ‘omics data to identify novel therapeutic targets. It could be predicting transcriptional change for a given disease-causing variant. It could be simulating the effect of modulating a target of interest. AI Infrastructure underpins the success of the AI Accelerator: the scale, speed and cost of every model trained or served rests on the compute, storage and platform substrate deployed. This includes getting the best from existing on-prem capabilities as well as securing best-value cloud capabilities., This is a hybrid role with approximately 4 days a week in the office. WHY THIS IS A GREAT PLACE TO WORK Boehringer Ingelheim has been recognised as a Top Employer in the UK, demonstrating our commitment to building an exceptional workplace through strong people practices and supportive HR policies. To learn more about why BI is a great place to work, visit