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 |
|---|---|---|
| Applied AI Researcher - World Reconstruction and Generation NVIDIA is seeking an Applied AI Researcher to work on NuRec-related research in world reconstruction and generation, developing and adapting Deep Learning-based methods for tasks like novel view synthesis, generative modeling, and neural rendering. The role involves prototyping with Python/PyTorch, building evaluation and agentic AI-assisted research workflows, and turning research into usable technology. Requires a PhD or MS with significant experience in ML/DL, Computer Graphics, Computer Vision, or 3D reconstruction, with strong Python/PyTorch skills. | Post-trainAgent | 9 |
| Senior Applied Deep Learning Scientist - Large Vision Language Models NVIDIA is seeking a Senior Applied Deep Learning Scientist to work on multimodal language models, specifically the Nemotron Omni family. The role involves pushing the boundaries of these models for downstream applications, preparing large-scale multimodal datasets, and collaborating globally to turn research into impactful products. The position spans the full pipeline from pre-training to post-training, with a focus on open models, weights, and data for real-world applications. |
| Post-trainData |
| 9 |
| Research Scientist, 3D Computer Vision Research Scientist role focused on 3D computer vision and deep learning, involving novel technique research, publication, and collaboration. Requires a Ph.D. and a strong publication record in top venues. | Pretrain | 9 |
| Research Scientist, 3D Computer Vision Research Scientist role focused on 3D computer vision and deep learning for scene understanding, pose estimation, and localization. The role involves publishing original research, mentoring, and collaborating with productization teams. Requires a Ph.D. and a strong publication record in top computer vision venues. | Post-train | 9 |
| Robotics Research Intern - 2026 Robotics Research Intern at NVIDIA focusing on fundamental and applied research across the full robotics stack, including perception, planning, control, reinforcement learning, imitation learning, and simulation. The goal is to transform research paradigms, transfer into products, and create new markets. | AgentData | 9 |
| Senior HPC and AI Networking Performance Research and Analysis Engineer Research Engineer focused on analyzing and optimizing the performance of large-scale distributed LLM training and inference on GPU clusters, with a strong emphasis on networking aspects. | PretrainServe | 8 |