Value Engineering and Outcomes Engineer

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Die Position

Job description

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. You will ensure that workloads are technically executable, scalable, and aligned with platform capabilities, enabling rapid time-to-value and sustainable adoption, and ensuring alignment between business intent and platform execution.

You will also contribute to defining and continuously improving how use cases move from concept to execution across the AI Factory and HPC ecosystem, helping to establish repeatable and scalable pathways for workload execution.

Description of the area

Hosting and Infrastructure (HI) provides mission-critical on-premise infrastructure, cloud hosting, connectivity, and technology products that enable all functions at every Roche site to develop, innovate, connect, and deliver compliant digital products across the Roche Enterprise.

The Value Streams - Accelerated Compute Engineering (ACE) Team is focused on driving both customer success and platform success by acting as a center of excellence and delivery for the High Performance Compute and AI Infrastructure supporting AI and HPC use cases across Roche. This team facilitates seamless onboarding and adoption for business vertical customers needing accelerated compute—helping those infrastructure consumers with needs optimized for high availability, seamless data transfer, flexibility, speed, and the rapidly changing needs of AI—helping achieve rapid time-to-value.

Within Accelerated Compute Engineering (ACE), the Value Engineering& Outcomes (VEO) team plays a critical role in the AI Factory ecosystem by ensuring that AI and HPC use cases are translated into executable workloads and successfully realized on platform infrastructure. Acting as a bridge between business domain teams, RDT AI teams (such as Applied AI), platform engineering, and infrastructure, the VEO team ensures that demand entering the AI Factory is structured, governed, and aligned with platform capabilities, enabling effective onboarding, execution, and measurable outcomes.

Job Responsibilities

Use Case Structuring, Challenge& Readiness

Partner with business domain teams and RDT AI teams (such as Applied AI) to clarify, structure, and constructively 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 not only defined, but 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 (such as Applied AI), 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

Wer wir sind

Eine gesündere Zukunft treibt uns zur Innovation an. Mehr als 100.000 Mitarbeiter weltweit arbeiten gemeinsam daran, wissenschaftliche Fortschritte zu erzielen und sicherzustellen, dass jeder Zugang zur Gesundheitsversorgung hat – heute und für zukünftige Generationen. Durch unser Engagement werden über 26 Millionen Menschen mit unseren Medikamenten behandelt und mehr als 30 Milliarden Tests mit unseren Diagnostik-Produkten durchgeführt. Wir ermutigen uns gegenseitig, neue Möglichkeiten zu erkunden, Kreativität zu fördern und hohe Ziele zu setzen, um lebensverändernde Gesundheitslösungen zu liefern.

Gemeinsam können wir eine gesündere Zukunft gestalten.

Roche ist ein Arbeitgeber, der die Chancengleichheit fördert.