Supplier Quality Engineer jobs at Nvidia in Vietnam, Thái Nguyên

What You’ll Be Doing:

• Design and prototype deep learning models for wireless signal processing tasks such as channel estimation, beam alignment, link adaptation, and scheduling.

• Work with simulation tools and real-world datasets to build models that generalize across diverse wireless scenarios.

• Implement, train, and validate neural networks (e.g., CNNs, Transformers, GNNs) using PyTorch or TensorFlow.

• Collaborate with researchers and system engineers to integrate models into full-stack RAN.

• Optimize model performance for real-time inference and hardware acceleration.

• Contribute to model evaluation, benchmarking, and deployment readiness on GPU platforms.

What We Need to See:

• MS or PhD in Electrical Engineering, Computer Engineering, or a related field (or equivalent experience).

• 12+ years of experience in wireless communications, signal processing, or AI/ML.

• Deep understanding of communication systems (e.g., MIMO, OFDM, fading channels) and DSP fundamentals.

• Strong experience in training and deploying deep learning models for time-series or signal-based tasks.

• Proficiency in Python and experience with DL frameworks like PyTorch or TensorFlow.

• Familiarity with tools such as MATLAB, GNU Radio, or NVIDIA Sionna for wireless simulation.

Ways to Stand Out from the Crowd:

• Experience with AI for 5G/6G systems, AI-for-RAN architecture, or telecom-grade deployments.

• Knowledge of channel estimation by AI, model compression, real-time inference, or GPU optimization

• Exposure to CUDA, Triton, or real-time inference pipelines.

• Contributions to research publications or open-source wireless/AI projects.

You will apply your expertise to develop highly available services that make effective use of the thousands of GPU involved in this operation. Your services provide the best-in-class performance, accuracy and availability. We are looking for technical talent to design, build, operate and improve our capabilities to produce NIMs at scale, including the underlying infrastructure, pipelines, inference backends, Docker build, test harness, metrics, performance engineering, log ingestion, and more.

What you'll be doing:

• Design, build, and optimize containerized inference execution for various AI applications, ensuring efficiency and scalability. These applications may run in container orchestration platforms like Kubernetes to enable scalable and robust deployment.

• Develop and deploy automation applications and microservices (e.g., in Python, Go) supporting the NIM factory.

• Ensure the performance, scalability, and availability of NIMs and the automation infrastructure through comprehensive performance measurement, monitoring, and optimization.

• Implement and manage CI/CD pipelines for automated testing and deployment.

• Apply container and orchestration expertise (Docker, Kubernetes) to create and optimize the basic building blocks of NIMs and automation tooling.

• Collaborate, brainstorm, and improve the designs of inference solutions with a broad team of software engineers, researchers, SREs, and product management.

• Mentor and collaborate with team members and other teams to foster growth and development. Demonstrate a history of learning and enhancing both personal skills and those of colleagues.

What we need to see:

• A history of using advanced programming skills (e.g., Python, Go) to build distributed compute systems, backend services, microservices, and cloud technologies.

• Experience productionizing and deploying various types of AI models (e.g., foundation models, computer vision, speech recognition).

• Experience implementing robust CI/CD pipelines for automated testing and deployment.

• Effective experience working with multi-functional teams, principals, and architects across organizational boundaries.

• Mentorship and the ability to grow teams and team members.

• Deep technical expertise in distributed containerized applications using Docker, Kubernetes, Cloud Endpoints, Helm, and Prometheus.

• Passion for building scalable and performant microservice applications.

• Excellent interpersonal skills and the flexibility to lead multi-functional efforts.

• Proven experience debugging and analyzing the performance of distributed microservices or cloud systems.

• A degree in Computer Science, Computer Engineering, or a related field (BS or MS) or equivalent experience.

• 1+ years of demonstrated experience in developing performant microservices, cloud software, and/or tooling roles.

Ways to stand out from the crowd:

• Experience with multiple container engines, internals of the container image and runtime.

• Prior experience in building and deploying containers for Microservices, Cloud, and On-prem deployments.

• Background with large-scale full-stack development.

• Experience delivering event-driven applications using services such as Temporal, Kafka, Redis, or similar.

• Previous work in large-scale backend development.

• Develop efficient infrastructure and tools for automating complex software processes.

• Drive Performance Optimization: Implement advanced test harnesses, benchmarking frameworks, and analytical tools to rigorously characterize and optimize the performance and efficiency of our software and hardware platforms.

• Apply deep knowledge of operating systems, kernel internals, device drivers, memory management, storage, networking, and high-speed interconnects to build and troubleshoot highly performant systems.

• Work with engineering teams to understand needs, define requirements, and deliver efficient solutions.

• Set performance goals, monitor feedback, analyze data, and make continuous improvements for system reliability.

• Influence Technical Strategy: Contribute to defining technical strategies and roadmaps for our platform automation initiatives, ensuring alignment with company-wide goals and standard methodologies.

• Bachelor's or equivalent experience in Computer Science, Computer Engineering, or a related technical field, or Master's degree or equivalent experience in a similar field.

• 5+ years of industry experience in software development, focusing on infrastructure, distributed systems, automation, and/or performance engineering.

• Expertise in System-Level Programming: Proven ability to develop robust tools and automation using programming languages such as C++, Python, or Go.

• Deep Understanding of System Software: Experience with operating system internals, device drivers, memory management, and debugging performance issues in complex compute applications.

• Distributed Systems: Experience in designing, building, and operating large-scale distributed systems, with knowledge of networking protocols, cluster management, and high-performance interconnects.

• Automation and CI/CD Proficiency: Experience building and maintaining automated testing, benchmarking, and continuousintegration/continuousdeployment pipelines.

• Problem-Solving and Analytical Skills: Outstanding analytical, problem-solving, and debugging skills, with a track record of resolving complex technical challenges.

• Collaboration and Communication: Excellent interpersonal and communication skills, with the ability to articulate complex technical concepts to diverse audiences and collaborate effectively across teams.

• Experience optimizing performance for AI/Machine Learning workloads, especially inference applications, on diverse hardware platforms.

• Prior experience building or contributing to large-scale compute infrastructure solutions in cloud environments or on-premises data centers.

• Experience with containerization and orchestration technologies, such as Docker and Kubernetes.

• Familiarity with performance profiling tools and methodologies for hardware and software systems.

• Track record of driving significant efficiency gains or architectural improvements in large-scale systems.

You will apply your expertise to develop highly available services that make effective use of the thousands of GPU involved in this operation. Your services provide the best-in-class performance, accuracy and availability. We are looking for technical talent to design, build, operate and improve our capabilities to produce NIMs at scale, including the underlying infrastructure, pipelines, inference backends, Docker build, test harness, metrics, performance engineering, log ingestion, and more.

What you'll be doing:

• Design, build, and optimize containerized inference execution for various AI applications, ensuring efficiency and scalability. These applications may run in container orchestration platforms like Kubernetes to enable scalable and robust deployment.

• Develop and deploy automation applications and microservices (e.g., in Python, Go) supporting the NIM factory.

• Ensure the performance, scalability, and availability of NIMs and the automation infrastructure through comprehensive performance measurement, monitoring, and optimization.

• Implement and manage CI/CD pipelines for automated testing and deployment.

• Apply container and orchestration expertise (Docker, Kubernetes) to create and optimize the basic building blocks of NIMs and automation tooling.

• Collaborate, brainstorm, and improve the designs of inference solutions with a broad team of software engineers, researchers, SREs, and product management.

• Mentor and collaborate with team members and other teams to foster growth and development. Demonstrate a history of learning and enhancing both personal skills and those of colleagues.

What we need to see:

• A history of using advanced programming skills (e.g., Python, Go) to build distributed compute systems, backend services, microservices, and cloud technologies.

• Experience productionizing and deploying various types of AI models (e.g., foundation models, computer vision, speech recognition).

• Experience implementing robust CI/CD pipelines for automated testing and deployment.

• Effective experience working with multi-functional teams, principals, and architects across organizational boundaries.

• Mentorship and the ability to grow teams and team members.

• Deep technical expertise in distributed containerized applications using Docker, Kubernetes, Cloud Endpoints, Helm, and Prometheus.

• Passion for building scalable and performant microservice applications.

• Excellent interpersonal skills and the flexibility to lead multi-functional efforts.

• Proven experience debugging and analyzing the performance of distributed microservices or cloud systems.

• A degree in Computer Science, Computer Engineering, or a related field (BS or MS) or equivalent experience.

• 6+ years of demonstrated experience in developing performant microservices, cloud software, and/or tooling roles.

Ways to stand out from the crowd:

• Experience with multiple container engines, internals of the container image and runtime.

• Prior experience in building and deploying containers for Microservices, Cloud, and On-prem deployments.

• Background with large-scale full-stack development.

• Experience delivering event-driven applications using services such as Temporal, Kafka, Redis, or similar.

• Experience with deploying AI inferencing workloads, benchmarking and testing AI models.

NVIDIA Vietnam R&D Center is an integral part of NVIDIA global network of world class Engineers and Researchers. To help push the boundary of Accelerated Computing, we’re seeking a hands-on technical leader to architect, build, and operate a platform for AI inference and agentic applications. You’ll focus on heterogeneous compute (with a strong GPU emphasis), reliability, security, and developer experience across cloud and hybrid environments.

What you will do:

• 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

• 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, andinfrastructure-as-codestandards.

• Lead through influence: write design docs, conduct reviews, mentor engineers, and shape platform roadmaps aligned to AI product needs.

What we need to see:

• 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/Kubernetesexpertise, cloudnetworking/storage/IAM,andinfrastructure-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.

Ways to stand out from a crowd:

• Deep GPU ops: MIG partitioning, MPS sharing, NUMA/topology awareness, DCGM telemetry, GPUDirect RDMA/Storage.

• Inference platform exposure: serving runtimes, caching/batching, autoscaling patterns, continuous delivery (agnostic to specific stacks).

• Agentic platform exposure: workflow engines, tool orchestration, policy/guardrails for tool access and data boundaries.

• Traffic/data plane: gRPC/HTTP/Protobuf performance, service mesh, API gateways, CDN/caching, global traffic management.

• Tooling:Terraform/Helm/GitOps,Prometheus/Grafana/OpenTelemetry,policy engines; bare-metal provisioning experience is a plus.

You will work alongside experienced engineers to develop, test, and maintain components that ensure the efficient production and deployment of AI models at scale. This is a fantastic opportunity to gain hands-on experience with cloud-native technologies, large-scale distributed systems, and the AI development lifecycle on thousands of GPUs.

What you'll be doing:

• Develop and deploy containerized applications and microservices (e.g., in Python, Go) supporting NIM production and automation, with guidance from senior engineers.

• Contribute to building and optimizing containerized inference execution for various AI applications.

• Assist in ensuring the performance and scalability of NIMs and automation tools through testing, monitoring, and analysis.

• Contribute to the implementation and maintenance of CI/CD pipelines for automated testing and deployment processes.

• Utilize container technologies (Docker) and orchestration platforms (Kubernetes) in development and deployment tasks.

• Support the enhancement of services for cloud environments, applying Infrastructure as Code (IaC) practices (e.g., Terraform, Ansible).

• Collaborate with team members to troubleshoot issues, debug code, and improve existing systems.

• Actively participate in team discussions, code reviews, and contribute to a collaborative engineering culture.

What we need to see:

• A Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field (or equivalent experience).

• 2+ years of software development experience (internships and significant academic projects count!).

• Solid programming fundamentals, with experience in languages like Python or Go.

• Familiarity with basic software development concepts (e.g., version control with Git, testing).

• An understanding of or strong interest in learning about containerization (Docker) and orchestration (Kubernetes).

• Interest in cloud technologies and distributed systems.

• A passion for problem-solving and eagerness to learn new technologies.

• Good communication and collaboration skills.

• Familiar with deploying and using AI Models, LLM, VLM.

Ways to stand out from the crowd:

• Prior internship experience in software development, cloud computing, or infrastructure.

• Experience with deploying AI inferencing workloads, benchmarking and testing AI models.

• Exposure to CI/CD tools (e.g., GitLab CI).

• Basic understanding of microservice architectures.

• Contributions to open-source projects.

What you will be doing:

• Optimize deep learning models for low-latency, high-throughput inference.

• Convert and deploy models using frameworks such as TensorRT and TensorRT-LLM

• Understand, analyze, profile, and optimize performance of deep learning workloads on state-of-the-art hardware and software platforms.

• Collaborate with internal and external researchers to ensure seamless integration of models from training to deployment.

What we want to see:

• Master’s or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related field (or equivalent experience)

• 5+ years of professional experience in deep learning or applied machine learning.

• Strong foundation in deep learning algorithms, including hands-on experience with LLMs and VLMs

• Deep understanding of transformer architectures, attention mechanisms, and inference bottlenecks.

• Proficient in building and deploying models using PyTorch or TensorFlow in production-grade environments.

• Solid programming skills in Python and C++

Ways to stand out from the crowd:

• Proven experience deploying LLMs or VLMs at scale in real-world applications.

• Hands-on experience with model optimization and serving frameworks, such as: TensorRT, TensorRT-LLM, vLLM, SGLang.