NVIDIA
- HQ
- California, US
- Founded
- 1993
- Size
- 35,000+
- Website
- nvidia.com
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).
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.
| Title | Stage | AI score |
|---|---|---|
| Senior Deep Learning Algorithms Engineer - BioNeMo Senior Deep Learning Algorithms Engineer at NVIDIA to optimize biology and structural biology models (LLMs, VLMs) for inference performance on GPUs using TensorRT-LLM and related stacks. Focus on low-latency, high-throughput inference, quantization, custom GPU kernels, and production deployment. | ServePost-train | 9 |
| AI Safety Scientist, Deep Learning Research Scientist focused on AI safety for multilingual, multimodal LLMs, including content safety, ML fairness, bias detection, and hallucination mitigation. The role involves developing datasets, moderator models, and training techniques (SFT, RL), and contributing to safety tools. | Post-train |
| 9 |
| Senior Deep Learning Algorithm Engineer Senior Deep Learning Algorithm Engineer at NVIDIA focused on optimizing deep learning training and inference workloads on state-of-the-art hardware and software platforms. The role involves performance analysis, profiling, and implementation of production-quality software, with a focus on squeezing performance from hardware and software stacks. | ServePost-train | 9 |
| Senior Product Engineer, Agentic AI Product leader and builder to define and drive Agentic AI products and platforms, bridging product vision with deep technical execution. Focus on translating early-stage innovations into scalable, production-ready systems for agentic AI workflows. | Agent | 8 |
| Senior Deep Learning Scientist, Speech Synthesis NVIDIA is seeking a Senior Deep Learning Scientist to work on their Speech AI product, Riva. The role involves training speech synthesis models (mel-spectrogram and vocoder), measuring and analyzing model performance, maintaining the TTS evaluation system, and improving speech data processing and training set preparation. The ideal candidate has a Master's or PhD, 5+ years of ML/AI experience, strong Python and PyTorch skills, and hands-on experience training speech synthesis models. | DataPost-train | 8 |
| Deep Learning Algorithms Engineer - ACOT NVIDIA is looking for an AI Acceleration & Optimization Engineer to optimize the performance, scalability, and efficiency of AI models (LLMs, VLMs, diffusion, multimodal) on NVIDIA GPU platforms. The role involves profiling, identifying bottlenecks, and applying optimization techniques like quantization and kernel fusion, using tools such as CUDA, TensorRT, and Nsight. Collaboration with various teams (algorithms, systems, hardware, research, CUDA, compiler, frameworks) is key to bringing models from research to production. | ServePost-train | 8 |
| Senior Deep Learning Engineer - AI for Wireless Systems NVIDIA is seeking a Senior Deep Learning Engineer to develop AI-native wireless networks, integrating deep learning into signal processing and radio access technologies. The role involves designing, prototyping, implementing, training, and optimizing deep learning models for real-time inference and deployment on GPU platforms, collaborating with researchers and system engineers. | ServePost-train | 8 |
| Engineering Manager - AI for RAN and 6G Wireless Systems NVIDIA is seeking an Engineering Manager to lead a team developing AI/ML models for 6G wireless networks. The role involves guiding model development, training, evaluation, and deployment, with a focus on integrating deep learning into signal processing and radio access technologies. Experience with Python, PyTorch/TensorFlow, and leading engineering teams is required. | ServePost-train | 8 |
| Director, Engineering – Software Engineering and AI Inferencing Platforms NVIDIA is seeking an Engineering Director to lead and scale software engineering teams in Vietnam, focusing on AI Inferencing Platforms and AI data/factory initiatives. The role involves driving the design, architecture, and delivery of high-performance system software platforms, collaborating with global teams, and overseeing the development and optimization of AI delivery platforms like NIMs and Blueprints. Experience with cloud, data, accelerated computing, and managing large AI/ML product teams is required. | ServeData | 8 |
| NBU Manufacturing Test Engineer NVIDIA is seeking a Manufacturing Test Engineer to design tools for product definition, data collection, test case execution, and results analysis. The role involves driving NBU diagnosis, analyzing test issues, qualifying equipment, and automating NBU product testing using AI and machine learning techniques. The engineer will also provide feedback on debug tools and support NBU product test setup. | Serve | 7 |
| Senior System Software Engineer - AI Data Platform - Inference Factory Optimization Senior Software Engineer focused on building and optimizing infrastructure for automating the deployment and performance tuning of NVIDIA's AI software offerings, impacting inference applications across various hardware. | Serve | 7 |
| SWQA Development Engineer Software QA Development Engineer at NVIDIA responsible for test planning, execution, and reporting, including writing scripts for test automation, designing and developing tools for the QA team, and developing integration tests. The role requires experience with AI development tools, model testing, and LLM benchmarking. | Eval Gate | 7 |