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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
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The Senior Research Manager, World Model Evaluation leads a team of Research Scientists focused on evaluating and benchmarking NVIDIA’s Physical AI model portfolio. This role involves building a research agenda for closed-system and open-system evaluations to define model utility, discover failures, and create improvement loops for data generation and training recipes. 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 position requires expertise in multimodal AI, robotics, and world models, specifically involving video models, diffusion models, and vision-language-action models. The work addresses the technical challenge of establishing rigorous standards for validity, reproducibility, and leakage prevention within complex machine learning systems and embodied AI workflows.
Lead a team of Research Scientists focused on world-model evaluation, benchmarking, and diagnostics for Physical AI models.
Define the scientific roadmap for closed-system and open-system evaluations, including metrics, failure taxonomies, and feedback loops.
Develop benchmarks for physical plausibility, temporal consistency, spatial reasoning, and long-horizon coherence.
Create mechanistic evaluation methods using model internals, such as representation probing, causal interventions, and activation analysis.
Drive evaluation-to-model-improvement loops to influence training recipes, data curation, and model roadmaps.
Publish high-quality research papers, technical reports, and open-source evaluation artifacts.
Establish rigorous standards for dataset hygiene, leakage prevention, and model comparison.
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.
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, research 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 or flow models, self-supervised learning, or world-model architectures.
Experience designing serious benchmarks, evaluation datasets, metrics, diagnostic tools, or model analysis frameworks for complex ML systems.
Familiarity with world-model evaluation and open-system analysis techniques, such as physical plausibility, temporal consistency, action conditioning, counterfactual reasoning, representation probing, activation patching, causal interventions, sparse autoencoders, or feature attribution.
Multimodal Foundation Models
LLMs
Large Vision-Language Models
Diffusion Algorithms
Action-based Transformers
PyTorch
Jax
TensorFlow
Python
C++
CUDA
Reinforcement Learning
Imitation Learning
ROS
Robot Kinematics
Model Predictive Control
MuJoCo
Isaac Sim
Whole-body Control
World Models
Video Diffusion
Neural Rendering
Gaussian Splatting
NeRFs
Flow-matching
Causal Video Generation
ML Infrastructure
CI/CD
Simulation
Robotics
Autonomous Vehicles
Synthetic Data
Cloud Services