This role pays more than
99%
of similar roles. Most pay
$199,950–$264,662
— the shaded band above.
At the midpoint, this role pays about
$352k
versus about
$232k
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.
Senior Research Manager, World Model Evaluation leads a team of Research Scientists focused on evaluation, benchmarking, and diagnostics for Physical AI models, including world foundation models, action models, and synthetic data generation systems. The role involves defining scientific roadmaps for closed-system and open-system evaluations to identify model failures and create feedback loops for training and data curation. Key responsibilities include developing benchmarks for physical plausibility, temporal consistency, and spatial reasoning while utilizing mechanistic evaluation methods like representation probing, causal interventions, activation analysis, and sparse autoencoders. The candidate will utilize expertise in machine learning, computer vision, robotics, and multimodal AI to improve world-action models and embodied AI workflows. This role addresses the technical challenge of defining what makes a world model useful for Physical AI through rigorous metrics, failure taxonomies, and high-quality research publications.
Lead a team of Research Scientists focused on world-model evaluation and diagnostics for Physical AI models.
Define the scientific roadmap for closed-system and open-system evaluations including metrics and failure taxonomies.
Develop benchmarks for physical plausibility, temporal consistency, scene dynamics, and action conditioning.
Create mechanistic evaluation methods using representation probing, causal interventions, and activation analysis.
Establish a feedback loop between evaluation findings and training, data curation, and model roadmap development.
Publish high-quality research papers, technical reports, and open-source evaluation artifacts.
Ensure rigorous standards for dataset hygiene, leakage prevention, and reproducible model comparisons.
What we're looking for
PhD or equivalent experience in Computer Science, Electrical Engineering, Robotics, Machine Learning, AI, or a related field.
Relevant research or engineering experience.
At least 5 years of management experience.
Strong research background in machine learning, computer vision, multimodal AI, robotics, world models, representation learning, model evaluation, or mechanistic interpretability.
Experience leading research teams, programs, or cross-functional technical initiatives with measurable scientific and product impact.
Deep understanding of modern foundation models including video models, vision-language-action models, diffusion/flow models, and self-supervised learning.
Experience designing benchmarks, evaluation datasets, metrics, diagnostic tools, or model analysis frameworks for complex ML systems.
Familiarity with open-system analysis techniques such as representation probing, causal interventions, activation analysis, and sparse autoencoders.
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
Multimodal Foundation Models
LLMs
Vision-Language Models
PyTorch
Jax
TensorFlow
Python
C++
CUDA
Reinforcement Learning
Imitation Learning
ROS
MuJoCo
Isaac Sim
Model Predictive Control
Robot Kinematics