New opportunity

Senior Developer Relations Manager - Cloud Provider AI Factory

NVIDIA · Singapore, Remote

About this role

The Senior Developer Relations Manager will serve as a strategic technical advisor and champion for the developer ecosystem within the cloud provider and hosting industry. This role focuses on driving the adoption of NVIDIA's advanced AI and computing platforms by working directly with software solution providers and industry professionals. Responsibilities include accelerating critical workloads by integrating the NVIDIA software stack (tools, libraries, SDKs, NIMs, and blueprints) into partner products and pipelines, guiding partners through onboarding, and mapping the ecosystem to identify growth opportunities. You will collaborate with engineering, product management, and marketing teams to inform roadmaps, resolve technical challenges, and evangelize NVIDIA technology. Required Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Minimum of 8 years of professional experience in software engineering, developer relations, technical partnerships, or solutions architecture, including at least 5 years of hands-on experience in cloud hosting or AI Factory infrastructure. Proven experience leading and scaling developer programs at major technology companies or cloud infrastructure ISVs. Significant technical depth in end-to-end AI Factory infrastructure, container runtimes, Kubernetes, hypervisors, hardware server design, and networking. Experience leading technical collaborations, including architectural design, code reviews, and technical mentorship. Preferred Qualifications: Hands-on experience building or optimizing vertical-specific solutions such as AI Factory cluster architecture, high-performance networking, Linux kernel tuning, or managed Kubernetes at scale. Familiarity with GPU acceleration platforms like CUDA, DOCA, vGPU hypervisors, and AI workload sandboxing. A successful history of building developer communities and delivering technical enablement programs is highly valued.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Cloud HostingAI Factory InfrastructureDeveloper RelationsKubernetesContainer RuntimesHypervisorsHardware ArchitectureNetworking DesignCUDADOCAGPU AccelerationTechnical EvangelismSoftware EngineeringTechnical PartnershipsSolutions Architecture

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Technical Integration of NVIDIA Software Stack
The core priority is accelerating partner adoption by integrating NVIDIA tools, libraries, SDKs, NIMs, and blueprints into partner products and pipelines. You must be prepared to provide hands-on technical guidance, including sample code and demo pipelines, to ensure seamless integration into cloud hosting environments.
Strategic Ecosystem Mapping and Feedback Loop
You are expected to map the developer ecosystem to identify growth opportunities and act as a conduit between external partners and internal NVIDIA product/engineering teams. This requires translating real-time developer sentiment and technical challenges into actionable product roadmap insights.
Technical Evangelism and Enablement
Serving as a subject matter expert, you must distill complex AI Factory infrastructure concepts for diverse audiences, from engineers to executives. This involves delivering technical talks, workshops, and documentation to foster co-innovation and drive best-practice adoption of NVIDIA technologies.

Likely challenges to think through

Balancing Technical Depth with Strategic Breadth
The role requires deep hands-on knowledge of Kubernetes, hypervisors, and hardware design while simultaneously managing high-level strategic partnerships. Prepare by demonstrating how you have previously navigated the tension between solving granular technical bugs and maintaining a long-term, scalable partner engagement strategy.
Navigating Complex Cross-Functional Stakeholder Management
You will need to orchestrate efforts across sales, legal, product, and marketing teams to execute technical integrations. Prepare for this by highlighting your experience in negotiating requirements and prioritizing competing interests from internal and external stakeholders to ensure project delivery.
Scaling Technical Enablement in Rapidly Evolving AI Domains
As AI Factory infrastructure evolves quickly, maintaining relevance is a challenge. Prepare by discussing your methodology for staying current with emerging technologies like CUDA or DOCA and how you have successfully built scalable programs that enable developers to adopt new technologies without constant direct intervention.

Practice your answers

Technical practice

Designing End-to-End AI Factory Infrastructure

What to coverExplain how you would architect a scalable AI Factory cluster, focusing on the integration of GPU acceleration, high-performance networking, and container orchestration. Discuss the trade-offs between different storage and networking topologies when optimizing for large-scale AI training and inference workloads.

Optimizing Kubernetes for GPU-Accelerated Workloads

What to coverDescribe your approach to managing Kubernetes at scale for AI workloads, specifically regarding GPU scheduling, resource isolation, and multi-tenancy. Address how you handle vGPU hypervisors and workload sandboxing to ensure performance and security in a cloud-hosting environment.

Integrating NVIDIA Software Stack into Partner Pipelines

What to coverHow do you guide a partner through integrating tools like NIMs, SDKs, and libraries into their existing CI/CD pipelines? Focus on the technical challenges of ensuring compatibility, performance tuning, and maintaining long-term support for these integrations.

Networking Design for High-Performance AI Clusters

What to coverDiscuss the critical networking requirements for AI Factory clusters, such as RDMA, InfiniBand, or high-speed Ethernet configurations. Explain how you troubleshoot bottlenecks in data movement between GPU nodes and storage systems.

Linux Kernel and Library Optimization

What to coverProvide an example of how you have tuned Linux kernel parameters or software libraries to improve application performance on accelerated hardware. Explain the methodology you use to identify performance regressions and validate improvements.

Leveraging CUDA and DOCA for Infrastructure Efficiency

What to coverExplain the role of CUDA and DOCA in modern AI infrastructure. Discuss how you would advise a partner on choosing between these technologies to solve specific infrastructure challenges, such as offloading network processing or accelerating data-intensive tasks.

Inference Hosting and Serverless Scaling

What to coverDescribe the architectural considerations for deploying inference services in a cloud-native, serverless environment. Address how you balance latency, throughput, and cost-efficiency when scaling these services for diverse end-user demands.

Technical Evangelism and Knowledge Transfer

What to coverHow do you distill complex technical concepts, such as AI model deployment or hardware architecture, for different audiences ranging from software engineers to C-suite executives? Suggestion: Focus on your ability to create actionable technical documentation, demo pipelines, and workshops that drive adoption.

Behavioural practice

Managing Complex Technical Engagements

What to coverDescribe a situation where you had to lead a multi-stakeholder technical project with conflicting requirements. Explain how you negotiated priorities between internal engineering teams and external partners to ensure a successful integration.

Translating Developer Feedback into Product Strategy

What to coverGive an example of how you captured developer sentiment or identified a market shift and successfully communicated this to product management. Detail the specific actions you took to influence the product roadmap based on this field intelligence.

Resolving Critical Technical Roadblocks

What to coverTell me about a time you encountered a significant technical challenge while helping a partner adopt a new technology. Describe your troubleshooting process, how you collaborated with internal experts, and the final outcome for the partner.

Building and Scaling Developer Communities

What to coverDescribe your experience in building or scaling a developer program. Focus on how you measured the success of the program and the specific strategies you used to foster co-innovation and long-term engagement within the ecosystem.

Sharpen your knowledge

Topics to prepare

AI Factory Infrastructure Architecture
Review the end-to-end components of an AI Factory, specifically focusing on the integration of hardware server design, high-performance networking, and container orchestration. Exercise: Map out a high-level architecture diagram showing how GPU-accelerated clusters interact with cloud-native storage and networking layers to support large-scale AI workloads.
Developer Ecosystem Strategy
Understand the lifecycle of developer programs, from initial onboarding to long-term technical enablement. Exercise: Draft a framework for capturing and analyzing developer sentiment and technical feedback from ISV partners to inform product roadmap decisions.

Skills to revise

Kubernetes and Container Runtime Optimization
Focus on managed Kubernetes implementations at scale within cloud hosting environments. Exercise: Review best practices for tuning container runtimes and Kubernetes operators to optimize GPU utilization and performance for AI inference workloads.
Technical Evangelism and Communication
Practice distilling complex technical concepts, such as CUDA or vGPU hypervisors, for diverse audiences ranging from software engineers to executive stakeholders. Exercise: Prepare a 5-minute technical pitch explaining the value proposition of integrating NVIDIA NIMs into a partner's existing cloud pipeline.
Linux Kernel and Library Performance Tuning
Gain hands-on familiarity with optimizing Linux environments for high-performance computing. Exercise: Research common bottlenecks in AI workload execution and identify specific Linux kernel parameters or library configurations that improve throughput in GPU-accelerated environments.

Your preparation checklist

Map AI Factory Infrastructure Expertise
Prepare a technical summary of your experience designing or managing end-to-end AI Factory infrastructure. Focus on your ability to integrate container runtimes, Kubernetes, and hypervisors within cloud hosting environments, specifically highlighting how you have optimized hardware server design and networking for high-performance AI workloads.
Develop a Technical Enablement Case Study
Create a structured walkthrough of a past developer program or technical partnership you led. Detail how you identified a technical bottleneck, the specific NVIDIA-adjacent tools or SDKs you implemented to resolve it, and the measurable impact on partner adoption or pipeline efficiency.
Prepare for Architectural Deep-Dives
Be ready to discuss the integration of GPU acceleration platforms like CUDA, DOCA, or vGPU hypervisors. Prepare to explain how you would guide an ISV partner through the technical onboarding process, from initial architectural design and code reviews to production-level deployment.
Formulate Strategic Ecosystem Insights
Draft a brief analysis of current market shifts in cloud-native AI inference hosting. Prepare to discuss how you would capture developer sentiment and translate those insights into actionable feedback for internal product and engineering teams to influence future roadmaps.
Prepare Targeted Questions for Leadership
Develop questions that demonstrate your strategic mindset, such as asking how the team currently balances short-term partner integration needs with long-term AI Factory infrastructure innovation, or how cross-functional collaboration is structured between DevRel, product marketing, and engineering at NVIDIA.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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