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.
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 Research Engineer, you will chip in across model development and runtime systems, building capabilities and helping turn 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, help 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:
Contribute to building and optimizing 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.
Help 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.
Deliver capabilities such as multi-user experiences and simulation workflows from prototype through evaluation, integration, and release.
Strengthen the open-source platform through testing, CI/CD, observability, documentation, and developer workflows, and partner with researchers and users to improve reliability and adoption.
What we need to see:
Experience or coursework 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.
Pursuing or recently completed a BS, MS or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field or equivalent experience.
Hands-on experience building, evaluating, integrating, optimizing, or serving machine-learning systems through internships, academic research, open-source work, or substantial projects.
Strong Python and PyTorch skills, supported by software-engineering fundamentals in design, testing, debugging, version control, performance analysis, and Linux development.
Ability to turn an open-ended technical problem into a working implementation, measure its quality and performance, and communicate the results clearly.
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 profiling or optimizing ML workloads using CUDA, Triton, TensorRT, torch.compile, or similar tools, including work on latency, throughput, quantization, streaming, state or cache management, or multi-GPU execution.
Contributions to an open-source ML project or developer platform, such as implementing model support, improving performance, building tests and benchmarks, fixing difficult issues, writing documentation, or helping users adopt the technology.
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 www.nvidiabenefits.com/
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 108,000 USD - 178,250 USD for Level 1, and 124,000 USD - 195,500 USD for Level 2.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
- Pursuing or recently completed a BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, a related field, or equivalent experience
- Experience or coursework in video or world models, diffusion and generative modeling, model distillation and adaptation, simulation, robotics, computer vision, or real-time stateful machine-learning systems
- Hands-on experience building, evaluating, integrating, optimizing, or serving machine-learning systems through internships, academic research, open-source work, or substantial projects
- Strong Python and PyTorch skills
- Software-engineering fundamentals in design, testing, debugging, version control, performance analysis, and Linux development
- Ability to turn open-ended technical problems into working implementations, measure quality and performance, and communicate results clearly
- Experience with post-training generative video models, including distillation, self-forcing, action conditioning, or long-horizon memory
- Experience profiling or optimizing machine-learning workloads using CUDA, Triton, TensorRT, torch.compile, or similar tools
- Experience with latency, throughput, quantization, streaming, state or cache management, or multi-GPU execution
- Contributions to an open-source machine-learning project or developer platform
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.”
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