FlashDreams and FastGen are NVIDIA’s core technologies for turning video models into real-time world simulations. The stack spans model adaptation for faster generation and richer control, plus the execution layer that runs those models as responsive experiences. We develop and ship this technology in pursuit of generative worlds that people can explore and direct. The work can enable autonomous-driving simulation, robot policy development and testing, game worlds, medical simulation, and virtual training.
As a Senior Research Engineer, you will lead engineering across model development and runtime systems, building capabilities and turning research into systems that work in real applications. We work across the world-model ecosystem, from emerging startups to established model labs. If you want to collaborate with leading researchers, shape a new computing platform, and ship AI capabilities with real-world impact, we would love to hear from you.
What you'll be doing:
Build and optimize the continuous autoregressive serving loop, including per-step control inputs, model and KV-cache state management, GPU inference, frame streaming, and model integrations to speed-of-light.
Advance the production-ready world model frontier by working with researchers on few-step distillation, causal or autoregressive generation, reward fine-tuning, action conditioning, and long-horizon spatiotemporal memory and consistency.
Lead end-to-end delivery of capabilities such as multi-user experiences and simulation workflows, from prototype through evaluation, integration, and release. Partner with applied researchers and domain teams to meet quality, performance, and reliability goals.
What we need to see:
Experience in one or more areas such as video or world models, diffusion and generative modeling, model distillation and adaptation, simulation, robotics, computer vision, or real-time, stateful ML systems.
MS or PhD in Computer Science, Electrical Engineering, or a related field (or equivalent experience).
5+ years of equivalent experience in applied ML or research engineering.
A record of advancing applied ML or ML systems through research, open-source software, patents, or deployed technology, including taking ambiguous ideas through thorough evaluation and release.
Hands-on experience with Python, PyTorch and GPU-accelerated training, inference, performance optimization, or serving.
Research and engineering judgment, with the ability to find practical solutions to open-ended problems and deliver reliable results within software and hardware constraints.
Ways to stand out from the crowd:
Experience with post-training generative video models, including distillation, self-forcing, action conditioning, or long-horizon memory.
Experience building and optimizing real-time, stateful generative inference systems, including history and KV-cache management, GPU kernels, quantization, parallel execution, streaming, scheduling, or multi-user serving.
Technical stewardship of an open-source ML project used by researchers or developers, with work spanning its architecture, APIs, model ecosystem, releases, maintainer practices, documentation, benchmarks, adoption, or community. Relevant ecosystems include vLLM, SGLang, FastVideo, LightX2V, Diffusers, FlashInfer, and comparable platforms.
With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most hard-working and talented people in the world working for us. If you're creative and passionate about developing cloud services we want to hear from you!
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.
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.Skills Required
- Experience in video or world models, diffusion and generative modeling, model distillation and adaptation, simulation, robotics, computer vision, or real-time stateful ML systems
- MS or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent experience
- 5 or more years of equivalent experience in applied machine learning or research engineering
- Record of advancing applied ML or ML systems through research, open-source software, patents, or deployed technology
- Experience taking ambiguous ideas through evaluation and release
- Hands-on experience with Python and PyTorch
- Experience with GPU-accelerated training, inference, performance optimization, or serving
- Research and engineering judgment with the ability to solve open-ended problems and deliver reliable results within software and hardware constraints
- Experience with post-training generative video models, distillation, self-forcing, action conditioning, or long-horizon memory
- Experience building and optimizing real-time, stateful generative inference systems
- Experience with history and KV-cache management, GPU kernels, quantization, parallel execution, streaming, scheduling, or multi-user serving
- Technical stewardship of an open-source ML project used by researchers or developers
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
