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This role pays more than
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At the midpoint, this role pays about
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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
1814 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1204 roles with salary data.
The Senior Applied Research Scientist – AI Native Numerical Methods joins the team to develop AI-native scientific, engineering, and industrial simulations using accelerated computing. The role involves inventing numerical algorithms that combine machine learning with classical solvers to improve convergence, stability, and performance. Key responsibilities include building solver-in-the-loop pipelines, developing learned preconditioners, and creating AI-guided multigrid methods. The researcher will work with residual histories, meshes, and physics constraints to solve problems in thermal-fluid, electromagnetics, and digital twin workflows. Required technical skills include PyTorch, JAX, Python, and GPU computing, alongside expertise in PDEs, sparse linear algebra, iterative solvers, and finite element methods. The position focuses on integrating differentiable simulation with numerical solvers to enhance large-scale industrial simulations across various computational engineering platforms.
Research AI-assisted numerical methods to improve convergence, stability, and performance for large-scale simulations.
Invent learned coarse spaces, preconditioners, and AI-guided multigrid methods.
Develop differentiable solver components and hybrid numerical/ML algorithms.
Build solver-in-the-loop pipelines using residual histories, meshes, and physics constraints.
Define evaluation methods to measure convergence rates, failure rates, and end-to-end speedup.
Help define the research agenda for AI-native numerical methods and solver intelligence.
What we're looking for
PhD or equivalent experience in computer science, machine learning, scientific computing, applied mathematics, computational engineering, physics, or a related field.
5 years of relevant work or research experience.
Background in machine learning and scientific computing with evidence of connecting ML methods to numerical algorithms.
Experience with PyTorch, JAX, or comparable deep learning frameworks, plus Python and GPU computing.
Working knowledge of PDEs, sparse linear algebra, iterative solvers, preconditioning, finite element/volume methods, optimization, or differentiable programming.
Research record in scientific machine learning, numerical linear algebra, or differentiable simulation integrated with numerical solvers.
Communication skills to support collaboration across AI research, numerical methods, product, and production software teams.
Evidence that ML methods improved real numerical solvers through faster convergence, fewer failures, improved robustness, or lower cost on industrial-scale simulations (preferred).
CUDA
C++
Python
Physics Engines
Numerical Solvers
Reinforcement Learning
JAX
Isaac Sim
Isaac Lab
MuJoCo
PhysX
Warp
Newton
Performance Modeling
parallel-programming
Geometry Processing
Differentiable Simulation
Deep Learning
Python
C++
LLMs
Diffusion Models
Unsupervised Learning
Genomics
Proteomics
Drug Discovery
Bioinformatics
Molecular Dynamics
Chemical Engineering
Structural Biology
Neural Networks
Foundation Models
Multimodal Learning
Information Retrieval
Machine Learning
Physical AI
Dense Retrieval
CLIP
Embeddings
Active Learning
Data Curation
Lidar
Radar
Production-grade Software
Model Training Pipelines
Neural Search
Multimodal Indexing