Currently tracking 440 active AI roles, down 53% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $100k–$575k (avg $262k).
NVIDIA currently has 496 active AI-related job listings. The majority of these roles, 52%, are focused on serving infrastructure, with agents representing another significant segment at 23%. Engineering is the dominant function, with 441 positions. The United States leads hiring geographies with 287 roles, followed by China with 64. Frequent tech tags include model_serving, inference_infra, and agent_orchestration, suggesting a focus on deployment and management of AI models. Over the last 30 days, NVIDIA posted 214 new AI roles, a 27% decrease compared to the previous 30-day period.
NVIDIA currently has 487 active AI-related roles in our index. The most common open titles are: Deep Learning Performance Architect (4), Senior Deep Learning Performance Architect (4), AI Research Scientist (3), Developer Technology Engineer - AI (3), Manager, Deep Learning Algorithms (3). Most positions are in Engineering and Research.
NVIDIA's active AI hiring is concentrated in: serving infrastructure (54%), agents (21%), application (8%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
NVIDIA is hiring AI talent in: United States (286 roles), China (59 roles), Israel (50 roles), Germany (21 roles).
Job postings at NVIDIA most frequently reference: model serving, inference infra, agent orchestration, llm observability, multimodal.
In the past 30 days, NVIDIA has posted 110 new AI-related roles. That is a -50% change versus the prior 30 days (218 → 110).
| Title | Stage | AI score |
|---|---|---|
| Senior Architect - Server Performance NVIDIA is seeking architects to drive architectural performance for its next-generation AI server systems. This position demands a unique capability to bridge deep architectural knowledge, workload analysis, and hands-on silicon investigations. Candidates should be adept at working directly with silicon, high-level models, and simulators. Responsibilities include conducting performance investigations on both NVIDIA and competitive platforms, and developing targeted microbenchmarks to examine specific architectural aspects. The role does not heavily involve modeling tasks (functional or performance), though occasional focused assignments may arise. | Serve | 8 |
| Senior GPU System Architect NVIDIA is seeking a Senior GPU System Architect to design multi-GPU scale-up and scale-out datacenter systems for AI and HPC. The role involves architecting system topologies, defining interconnects (NVLink, Ethernet), collaborating on RDMA, using system models for analysis, and co-designing hardware-software stacks for efficient AI workload deployment. |
| Serve |
| 8 |
| Senior System Software Engineer, Speech AI NVIDIA is seeking an experienced Software Engineer to work on their GPU-accelerated Speech AI platform, focusing on building and optimizing core speech recognition (ASR), text-to-speech (TTS), and S2S services for real-time conversational AI applications. The role involves developing C++ & Python backend implementations, optimizing inference performance, adding new features, contributing to client libraries, and performance analysis of complex systems. | ServePost-train | 8 |
| System Software Engineer - Deep Learning System Software Engineer at NVIDIA focused on accelerating deep learning inference for autonomous driving systems using NVIDIA GPUs and DL accelerators. The role involves developing SDKs/frameworks for LLMs and state-of-the-art models, benchmarking, and optimizing for latency, accuracy, and power consumption. Requires experience with deep learning frameworks, DNN optimization, and C/C++. | ServePost-train | 8 |
| AI Developer Technology Engineer NVIDIA is seeking an AI Developer Technology Engineer to work on optimizing AI techniques on GPU architectures and collaborate with customers and internal teams to influence future designs. The role involves studying and developing cutting-edge deep learning, graphs, and machine learning techniques, with a focus on performance analysis and optimization for GPUs. The engineer will also work with customers to understand their problems and provide AI solutions using GPUs, and collaborate with NVIDIA's internal teams to shape next-generation architectures and software platforms. | Serve | 8 |
| GPU System Architect NVIDIA is seeking a GPU System Architect to design multi-GPU scale-up and scale-out datacenter systems for AI and HPC. The role involves defining system architectures that tightly couple GPU compute, memory, and interconnects for optimal AI performance, scalability, and resilience. Responsibilities include architecting system topologies, defining high-speed interconnects, collaborating on RDMA hardware, using system models for analysis, and enabling hardware-software co-design. | Serve | 7 |
| Senior System Software Engineer - Video Senior System Software Engineer role focused on building and optimizing system software for NVIDIA's video subsystem, involving AI/ML and computer vision algorithms for video compression and multimedia processing on Tegra Application Processors and GPUs. Requires strong C/C++ and Python skills, experience with video compression standards, and a track record in pre/post-processing algorithms. | Serve | 7 |
| Senior System Software Engineer - Computer Vision Algorithms and SDK Senior System Software Engineer focused on developing and optimizing computer vision, signal processing, and machine learning algorithms for specialized DSP hardware (PVA engine) and enhancing the associated SDK. The role involves working with internal and external customers to enable efficient algorithm development and optimization on the hardware. | Serve | 7 |
| Senior System Software Engineer - Windows DevOps and Test Labs NVIDIA is seeking a Senior System Software Engineer to build and maintain infrastructure for deploying AI applications and models on Windows. The role involves developing and sustaining infrastructure for AI workloads, scoping requirements for deploying AI applications, managing AI model repositories, analyzing data for insights, and collaborating with developers to debug issues. The engineer will also build CI/CD workflows and understand existing infrastructure. | Serve | 5 |