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.
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 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 |
|---|---|---|
| Machine Learning Applications and Compiler Engineer, LPX - New College Grad 2026 NVIDIA is seeking engineers to develop algorithms and optimizations for their LPX inference and compiler stack, working at the intersection of large-scale systems, compilers, and deep learning to optimize neural network workloads on future NVIDIA platforms. | Serve | 9 |
| Senior Software Engineer, AI Inference Systems NVIDIA is seeking a Senior Software Engineer to build and optimize AI inference systems for large-scale models, focusing on extreme efficiency and performance across multi-GPU, multi-node, and multi-cloud environments. The role involves architecting inference stacks, optimizing GPU kernels and compilers, driving benchmarks (MLPerf), and orchestrating large-scale deployments. | Serve |
| Senior Systems Software Engineer - Deep Learning Solutions Senior Systems Software Engineer focused on deep learning inference optimization for autonomous vehicles and robotics on edge hardware. The role involves analyzing and improving deep learning models on NVIDIA platforms, benchmarking performance, evaluating emerging model architectures, and collaborating with compiler, runtime, and hardware teams to deliver inference solutions. | Serve | 9 |
| DL System Software Engineer - AI Platform NVIDIA is seeking a DL System Software Engineer to join their AI Platform team. The role involves developing and building solutions for scheduling large-scale AI training and inference workloads on GPU clusters, optimizing performance and efficiency for large models. The engineer will work on core infrastructure, resource management, and GPU scheduling, contributing to NVIDIA's AI platform. | ServePost-train | 8 |
| GPU Performance Engineer - Neural Reconstruction GPU Performance Engineer focused on optimizing neural reconstruction and Gaussian Splatting workloads, involving PyTorch, CUDA, and GPU profiling to improve training and rendering performance. | ServePost-train | 8 |
| Machine Learning Applications and Compiler Engineer, LPX - New College Grad 2026 NVIDIA is seeking engineers to develop algorithms and optimizations for their LPX inference and compiler stack, working at the intersection of large-scale systems, compilers, and deep learning to optimize neural network workloads on future NVIDIA platforms. The role involves building and maintaining high-performance runtime and compiler components, defining workload mappings, integrating with the SW ecosystem, benchmarking, profiling, and collaborating with hardware teams. It also includes prototyping new compilation techniques and publishing technical work. | Serve | 8 |
| Senior Machine Learning Applications and Compiler Engineer, LPX NVIDIA is seeking a Senior Machine Learning Applications and Compiler Engineer to develop algorithms and optimizations for their LPX inference and compiler stack, working at the intersection of large-scale systems, compilers, and deep learning to map neural network workloads onto future NVIDIA platforms. | Serve | 8 |
| DL System Software Engineer - AI Platform NVIDIA is seeking a DL System Software Engineer to develop an AI Platform for efficient inference and training of large-scale models on GPU clusters. The role involves designing and building solutions for scheduling workloads, resource management, and performance optimization, working with various NVIDIA AI technologies. | ServePost-train | 8 |
| Senior Software Engineer, AI Inference Senior Software Engineer focused on optimizing and scaling AI inference for large language models, working with customers and contributing to open-source projects like vLLM. | Serve | 7 |
| Senior ASIC Methodology Engineer - LPU Division This role focuses on inventing and pioneering AI-driven and sophisticated automation techniques to transform the way ASICs are conceived, explored, and brought to closure, improving predictability, convergence, and turnaround time in the ASIC development lifecycle. The role involves identifying and leveraging data for AI models, establishing metrics, sharing best practices, and tracking advances in AI and hardware design research. | Serve | 7 |
| ASIC Methodology Engineer - New College Grad 2026 This role focuses on inventing and pioneering AI-driven automation techniques to transform ASIC development methodology, improving predictability, convergence, and turnaround time. The engineer will identify bottlenecks, curate data for AI models, establish metrics, share best practices, and track advances in AI and hardware design research. | Serve | 7 |