高级AI研究员 - 世界基础模型
Senior AI Researcher - World Foundation Models
NVIDIA 在过去 25 多年里一直在改变计算机图形学、个人电脑游戏和加速计算。这是一段独特的创新传统,由伟大的技术和令人惊叹的人才所驱动。如今,我们正在利用人工智能的无限潜力,定义计算的新时代。在这个时代,我们的 GPU 成为能够理解世界的计算机、机器人和自动驾驶汽车的大脑。做前所未有的事情需要远见、创新和世界上最优秀的人才。作为 NVIDIA 的一员,你将置身于一个多元、支持性的工作环境,每个人都能被激励做出最好的工作。加入我们的团队,看看你如何对世界产生持久的影响。
NVIDIA 正在构建下一代 AI 系统,这些系统可以感知、推理并生成动态世界。我们的团队推进世界基础模型,以实现物理 AI、模拟和交互体验的高保真、时间稳定的视频和世界生成。这个职位处于应用研究的边界:开发和验证模型改进,然后将其固化为生产级检查点和配方,供团队可靠地进行构建。技术重点在于人类外观、运动和动作的理解。进展通过有纪律的实验、稳健的诊断和可重复的对比评估来衡量。工作与数据、平台和产品工程紧密合作,以确保改进能转化为实际性能和质量。
你将负责:
- 研究、实现和验证改进视频生成保真度的模型架构和算法变更,重点关注以人类为中心的质量。
- 探索并原型化在空间多模态建模、模态对齐、基于流或扩散的视频生成以及神经渲染启发式表示方面的改进,以提高可控性和长时序一致性。
- 通过架构和训练后技术(计算/内存优化、蒸馏、剪枝和压缩)提高训练和推理效率。
- 定义提升 sim-to-real 和 real-to-sim 泛化的模型训练目标,特别是在人类运动、接触和交互动力学方面,适用于真实世界和合成/模拟数据。
- 开发详细的领域特定基准,用于评估世界基础模型,特别是生成和理解世界模型,这些模型能够对视频和模拟进行推理。
查看英文原文
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
NVIDIA is building the next generation of AI systems that can perceive, reason about, and generate dynamic worlds. Our team advances world foundation models to enable high-fidelity, temporally stable video and world generation for Physical AI, simulation, and interactive experiences. This role operates at the applied-research boundary: developing and validating model improvements, then hardening them into production-grade checkpoints and recipes that teams can reliably build on. The technical focus is on human appearance, motion and action understanding. Progress is measured through disciplined experimentation, robust diagnostics, and repeatable side-by-side evaluation. Work is delivered in close partnership with data, platform, and product engineering to ensure improvements translate into real performance and quality.
What you'll be doing:
- Research, implement, and validate model architecture and algorithm changes that improve video generation fidelity, with emphasis on human-centric quality.
- Explore and prototype improvements across spatial multimodal modeling, modality alignment, flow-based or diffusion-based video generation, and neural rendering-inspired representations to improve controllability and long-horizon consistency.
- Improve training and inference efficiency through architectural and post-training techniques (compute/memory optimizations, distillation, pruning, and compression).
- Define model training objectives that improve sim-to-real and real-to-sim generalization, especially for human motion, contact, and interaction dynamics across real-world and synthetic/simulation data.
- Develop detailed, domain-specific benchmarks for evaluating world foundation models, especially generation and understanding world models that reason about video, simulation, and physical environments.
- Translate research results into robust implementations like training code, production-grade checkpoints, model integrations, and demos that clearly showcase capability gains across teams.
What we need to see:
- PhD in Computer Science, Graphics, Computer Engineering, or a closely related field (or equivalent experience).
- 8+ years of applied research and/or industry experience in vision, graphics, or adjacent ML domains or similar area.
- 3+ years of direct experience designing, training, and evaluating generative models for image/video/audio, with strong fundamentals in modern deep learning.
- Hands-on experience improving generative models with a focus on perceptual quality and temporal stability, especially for generating humans.
- Advanced proficiency in Python, PyTorch, C++, and CUDA with strong research-engineering practices (reproducibility, testing, profiling, experiment tracking).
- Experience training and debugging large models in multi-GPU and/or multi-node environments and distributed training workflows
- Practical knowledge of inference/runtime bottlenecks and optimization techniques.
- Strong “eye for quality” and interest in diagnosing visual artifacts (sharpness, texture detail, temporal stability, etc.) using perceptual metrics, human preference signals, or learned evaluators.
Ways to stand out from the crowd:
- Proven track record in related research, including publications in top conferences (e.g., NeurIPS, CVPR, ICLR), with clear evidence of impact on model quality or robustness.
- Experience using agentic workflows, and AI coding companions, to accelerate research and production development, including code generation, debugging, test creation, experiment automation, benchmark development, documentation, and large-codebase navigation.
Join us to help build the next generation of world models! Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until June 27, 2026.This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Originally posted on Himalayas