Principal Engineer, System Software Platform Engineering at NVIDIA

Ho Chi Minh City, Vietnam

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Not SpecifiedCompensation

Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

AI, TechnologyIndustries

Requirements

15+ years building/operating large-scale distributed systems or platform infrastructure; strong record of shipping production services
Proficiency in one or more of Python/Go/Java/C++; deep understanding of concurrency, networking, and systems design
Containers/orchestration/Kubernetes expertise, cloud networking/storage/IAM, and infrastructure-as-code
Practical GPU platform experience: Kubernetes GPU operations (device plugin, GPU Operator, feature discovery), scheduling/bin-packing, isolation, preemption, utilization tuning
Virtualization background: deploying and operating vGPU, PCIe pass-through, and/or mediated devices in production
SRE or equivalent experience: SLOs/error budgets, incident management, performance tuning, resource management, and financial oversight
Security-first mentality: TLS/mTLS, RBAC, secrets, policy-as-code, and secure multi-tenant architectures

Responsibilities

Build and operate the platform for AI: multi-tenant services, identity/policy, configuration, quotas, cost controls, and paved paths for teams
Lead inference platforms at scale, including model-serving routing, autoscaling, rollout safety (canary/A-B), ensuring reliability, and maintaining end-to-end observability
Operate GPUs in Kubernetes: lead NVIDIA device plugins, GPU Feature Discovery, time-slicing, MPS, and MIG partitioning; implement topology-aware scheduling and bin-packing
Lead GPU lifecycle: driver/firmware/Runtime (CUDA, cuDNN, NCCL) updates via NVIDIA GPU Operator; ensure kernel/RHEL/Ubuntu compatibility and safe rollouts
Enable virtualization strategies: vGPU (e.g., on vSphere/KVM), PCIe passthrough, mediated devices, and pool-based GPU sharing; define placement, isolation, and preemption policies
Build secure traffic and networking: API gateways, service mesh, rate limiting, authN/authZ, multi-region routing, and DR/failover
Improve observability and operations through metrics, tracing, and logging for DCGM/GPUs, runbooks, incident response, performance, and cost optimization
Establish platform blueprints: reusable templates, SDKs/CLIs, golden CI/CD pipelines, and infrastructure-as-code standards
Lead through influence: write design docs, conduct reviews, mentor engineers, and shape platform roadmaps aligned to AI product needs

Skills

Kubernetes

GPU

CUDA

cuDNN

NCCL

NVIDIA GPU Operator

MPS

MIG

vGPU

vSphere

KVM

PCIe passthrough

service mesh

DCGM

CI/CD

infrastructure as code

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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