This role pays more than
68%
of similar roles. Most pay
$186,025–$254,750
— the shaded band above.
At the midpoint, this role pays about
$248k
versus about
$220k
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 Scientist, Fundamental Generative AI, you will join the fundamental generative AI research team at NVIDIA Research to push boundaries in generative models. You will research, design, and implement novel, large-scale generative AI methods while publishing original research and collaborating with internal teams and product groups. The role focuses on advancing generative AI methods for biomolecular design, specifically targeting small molecule, protein, and RNA design, as well as other scientific applications under the GenAI4Science initiative. To succeed, you must possess a strong mathematical foundation and expertise in building models for molecules, molecular dynamics, proteins, or RNA. Required technical skills include proficiency in Python, C++, CUDA parallel programming, and deep learning frameworks like PyTorch. You will translate complex research into technology for product groups while contributing to the field through conference presentations and publications.
What does a Research Scientist earn?
Median $216250 from 42 postings across 13 companies.
PyTorch
Generative AI
Reinforcement Learning
Physics Simulation
3D Generative Models
Distributed Training
Data Processing Pipelines
open-source
Robotics
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