This role pays less than
53%
of similar roles. Most pay
$179,475–$256,575
— the shaded band above.
At the midpoint, this role pays about
$216k
versus about
$218k
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.
Research Scientist, Generative AI for Physical AI - PhD New College Grad 2026 will join the Cosmos team to develop next-generation algorithms bridging virtual and physical realms. The role involves pioneering generative AI algorithms for physical AI applications, specifically focusing on advanced video generative models and video-language models. Responsibilities include architecting data processing pipelines for premium training data, developing physics simulation algorithms to enhance training, and scaling large-scale training systems across 20,000+ GPUs. Candidates must possess a PhD in a related field and demonstrate expertise in PyTorch, diffusion models, vision language and reasoning models, reinforcement learning, and 3D generative models. The work focuses on the technical challenge of integrating physics simulations with AI systems to create robust physical AI solutions while contributing to open-source initiatives and publishing research in major conferences like NeurIPS or CVPR.
What does a Research Scientist earn?
Median $216250 from 42 postings across 13 companies.
Generative AI
Multimodal Foundation Models
World Models
Python
PyTorch
Computer Vision
Reinforcement Learning
Distributed Training
Robotics
Autonomous Driving
Video Understanding
Simulation
VLMs
Policy Learning
Python
C++
CUDA
PyTorch
JAX
TensorFlow
Deep Learning
Generative AI
Computer Vision
Large Language Models
Diffusion Models
Multimodal Foundation Models
World Models
Action-Based Transformers
Physics-Based Simulation
Synthetic Data Generation
Partial Differential Equations
Python
C++
Generative AI
Reinforcement Learning
LLMs
PyTorch
TensorFlow
deep-learning
Agentic Systems
Distributed Training
Data Processing
Probabilistic Modeling
Statistics
Optimization Methods