As a member of the ACE Value Engineering &

- Outcomes (VEO) team, you will play a key role in ensuring that AI and High Performance Computing (HPC) use cases are successfully translated, deployed, and executed across Roche's AI Factory and HPC infrastructure. Operating at the intersection of business domains, RDT AI teams (such as Applied AI), and platform engineering, you will contribute to owning the end‑to‑end flow from use case intent to real, running workloads, ensuring technical feasibility, scalability, and alignment with platform capabilities.

Use Case

Structuring, Challenge &

- Readiness Partner with business domain teams and RDT AI teams to clarify, structure, and challenge AI and HPC use cases. Assess readiness, dependencies, and feasibility across data, infrastructure, and platform constraints. Ensure use cases are technically viable and aligned with platform capabilities before execution. Identify gaps early and guide teams toward executable pathways.

Workload

Translation, Architecture &

- Platform Routing Translate use cases into executable workload designs, including compute, storage, orchestration, and data requirements. Define how workloads are deployed across AI Factory, HPC, and hybrid environments. Leverage experience with containerized and distributed systems (e.g., Kubernetes, HPC schedulers) to ensure workloads are production‑ready. Develop reusable patterns to standardize workload deployment and scaling.

Platform

Onboarding &

- Execution Drive onboarding of workloads into platform environments, ensuring all technical prerequisites are met. Work closely with engineering and platform teams to ensure workloads are successfully deployed and running. Troubleshoot and resolve issues across the full stack,



from infrastructure to application behavior. Ensure workloads progress from onboarding to first successful execution.

Governance

Integration &

- Execution Pathways Embed governance, compliance, and prioritization frameworks into execution pathways, ensuring use cases are not only approved but operationally viable. Ensure governance decisions are reflected in how workloads are structured, routed, and executed. Act as a bridge between governance intent and real‑world platform execution. Help ensure that governance is defined and consistently applied through real execution practices. Outcomes, Performance &
- Scaling Ensure workloads progress to successful execution and measurable outcomes aligned with business needs. Identify performance, scaling, and reliability challenges in real‑world environments. Establish feedback loops to inform platform, architecture, and process improvements. Contribute to scaling patterns across multiple use cases and domains. Cross‑Functional Leadership Connect and align business domain teams, RDT AI teams, platform engineering, and infrastructure teams to enable successful workload execution. Influence decisions across organizational boundaries to ensure successful delivery. Provide clarity on execution pathways, risks, and constraints. Contribute to shaping how the AI Factory ecosystem operates end‑to‑end. Performance &




- Optimization Track and improve time‑to‑value from use case intake to first successful execution. Identify cross‑team bottlenecks and optimization opportunities across intake, translation, and execution. Contribute to continuous improvement of workflows and operating models.

Qualifications – Education / Experience Bachelor’s degree or advanced degree in Computer Science, Engineering, or a related discipline. Strong experience in AI/ML platforms or HPC environments. Hands‑on experience with containerized workloads and orchestration (e.g., Kubernetes, CaaS) and/or HPC scheduling environments. Proven ability to take workloads from concept to running systems. Comfortable working across infrastructure, platform, and application layers.

Experience collaborating with both technical teams and business/domain stakeholders.

Technical Skills

Understanding of AI/ML or HPC workload characteristics.

Experience with cloud and/or on‑premise compute environments. Familiarity with orchestration frameworks (Kubernetes, Slurm, etc.). Ability to diagnose and resolve issues in real runtime environments. Ability to connect technical solutions to business outcomes and use case needs. Strong systems thinking and problem‑solving skills.

Leadership Skills

Ability to influence without authority across engineering, AI, and business stakeholders. Strong ownership mindset, driving work through to execution and outcomes. Comfortable operating in ambiguity and shaping new ways of working. Enterprise mindset with strong collaboration across organizational boundaries. Bias toward action and solving real problems, not just defining them. Roche is an Equal Opportunity Employer.

📌 Value Engineering and Outcomes Engineer (Madrid)
🏢 JobFlurry
📍 Madrid