This role pays more than
59%
of similar roles. Most pay
$183,837–$254,750
— the shaded band above.
At the midpoint, this role pays about
$236k
versus about
$219k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
As a Senior Research Engineer, Interactive World Models, you will join the team developing FlashDreams and FastGen technologies to transform video models into real-time world simulations. You will lead engineering across model development and runtime systems, building capabilities such as multi-user experiences and simulation workflows from prototype through release. Your daily work involves optimizing continuous autoregressive serving loops, managing KV-cache states, and performing GPU inference for frame streaming. You will collaborate with researchers on few-step distillation, action conditioning, and long-horizon spatiotemporal memory to solve problems in autonomous driving, robotics, and medical simulation. The role requires expertise in Python, PyTorch, and GPU-accelerated training or inference. Candidates should possess experience in video models, diffusion, and generative modeling, while navigating hardware constraints to deliver reliable results within the world-model ecosystem for interactive environments.
Build and optimize the continuous autoregressive serving loop for real-time video generation.
Manage model and KV-cache states to improve GPU inference and frame streaming performance.
Collaborate with researchers on model distillation, reward fine-tuning, and action conditioning.
Develop systems for long-horizon spatiotemporal memory and consistency in world models.
Lead the end-to-end delivery of multi-user experiences and simulation workflows from prototype to release.
Partner with domain teams to meet specific quality, performance, and reliability goals.
Translate research concepts into production-ready systems for applications like robotics and autonomous driving.
What we're looking for
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.
Experience in areas such as video/world models, diffusion, generative modeling, distillation, simulation, robotics, computer vision, or real-time stateful ML systems.
A record of advancing applied ML or ML systems through research, open-source software, patents, or deployed technology.
Hands-on experience with Python, PyTorch, and GPU-accelerated training, inference, performance optimization, or serving.
Research and engineering judgment to find practical solutions to open-ended problems within hardware and software constraints.
Experience with post-training generative video models, including distillation, self-forcing, action conditioning, or long-horizon memory (preferred).
Experience building/optimizing real-time inference systems, GPU kernels, quantization, or technical stewardship of an open-source ML project (preferred).
World Models
Video Diffusion
Neural Rendering
Gaussian Splatting
NeRFs
Neural Reconstruction
Neural Shading
CI/CD
Simulation
Robotics
Autonomous Vehicles
Open Source
Production ML Infrastructure
Cloud Services
PyTorch
JAX
TensorFlow
Python
C++
CUDA
Kubernetes
SLURM
Deep Learning
Reinforcement Learning
LLMs
MLOps
HPC
Distributed Training
Sim-to-Real
Natural Language Processing
Graphics
CUDA
Python
C++
Rust
TensorRT-LLM
vLLM
SGLang
Triton
PyTorch
NVIDIA Nsight Systems
CUTLASS
NCCL
Quantization
Distributed Systems
GPU Architecture
Model Parallelism
Speculative Decoding