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 |
|---|---|---|
| Deep Learning Senior Engineer, End-To-End Autonomous Driving NVIDIA is seeking a Deep Learning Senior Engineer to design, implement, and deploy end-to-end autonomous driving systems. The role focuses on AI 2.0, leveraging LLMs, VLMs, and VLAs for reasoning and planning in autonomous vehicles and robotics. Responsibilities include training large-scale models, building and fine-tuning LLM/VLM/VLA systems, exploring data generation strategies, and deploying models in production environments, integrating them with vehicle firmware. | Post-trainAgent | 9 |
| LLM Reinforcement Learning Framework Engineer NVIDIA is seeking an LLM Reinforcement Learning Framework Engineer to develop and deploy RL algorithms for LLM post-training, focusing on improving reasoning and alignment. The role involves integrating RL components into NVIDIA's LLM stack, crafting experiments, and ensuring production readiness. Requires strong Python, PyTorch, and practical RL experience with LLMs, along with familiarity in async/distributed orchestration. |
| Post-trainAgent |
| 9 |
| Senior Software Engineer, RL Post-Training Frameworks NVIDIA is seeking a Senior Software Engineer to build and scale RL post-training infrastructure, focusing on distributed systems, high-performance computing, and deep learning infrastructure. The role involves architecting and optimizing RL training-inference-rollout loops, ensuring fault tolerance and elastic scaling, and collaborating with researchers and hardware teams. | Post-trainServe | 9 |
| Senior Machine Learning and Simulation Engineer - Autonomous Vehicles Senior ML Engineer focused on building and optimizing large-scale Reinforcement Learning (RL) training frameworks for multi-modal Autonomous Vehicle (AV) foundation models. This role involves designing simulation and data processing pipelines, refining reward functions, and ensuring the reliability of training workflows on GPU clusters, with a focus on closed-loop simulation for training end-to-end AV models. | Post-trainAgent | 9 |
| Deep Learning Senior Engineer, End-To-End Autonomous Driving NVIDIA is looking for a Deep Learning Senior Engineer to design, implement, and deploy end-to-end autonomous driving systems. The role focuses on leveraging LLMs, VLMs, and VLAs for reasoning and planning, involving model training, pre-training, fine-tuning, and integration into safety-critical vehicle firmware. Experience with production-grade ML models and C++ for deployment is required. | Post-trainAgent | 9 |
| Principal Deep Learning Senior Engineer, End-To-End Autonomous Driving NVIDIA is seeking a Principal Deep Learning Senior Engineer to design, implement, and deploy end-to-end autonomous driving systems. The role focuses on leveraging LLMs, VLMs, and VLAs for advanced reasoning and planning in vehicles and robotics, involving model training, pre-training, fine-tuning, and integration into safety-critical systems. | Post-trainAgent | 9 |
| Agent RL Infra Engineer NVIDIA is seeking an engineer to develop and productionize reinforcement learning (RL) capabilities for agent teams within an enterprise context. The role involves evaluating and adapting RL approaches, designing reward environments, operationalizing training backends, and integrating with existing ML services. Responsibilities include leading data curation, designing RL training loops, integrating with GPU infrastructure, building observability, and collaborating with various platform and customer teams. The ideal candidate has extensive experience in operationalizing fine-tuning and RL techniques, familiarity with distributed training frameworks and MLOps, and proficiency in relevant programming languages. | Post-trainAgent | 9 |
| Senior Research Engineer Neural Reconstruction Senior Research Engineer focused on neural reconstruction, developing and integrating neural rendering approaches for generative video, segmentation, and 3D reconstruction. The role involves adapting and fine-tuning generative models, collaborating on ML workflows, and contributing to core NVIDIA products. Requires strong Python and ML library skills, with experience in training and optimizing models. | Post-trainServe | 9 |
| Senior Scientific Machine Learning Engineer – Earth-2 Develops and enhances machine learning frameworks (NVIDIA PhysicsNeMo, NVIDIA Earth2Studio) for scientific ML technology in weather, climate, and earth system modeling. Focuses on implementing new deep learning techniques and enhancing Earth-2 technologies. | Post-train | 8 |