Senior AI Solutions Architect – Industrial Engineering

NVIDIA

Senior Solutions Architect helping industrial engineering software vendors and OEMs accelerate CAE/CFD/FEA simulations. Applying NVIDIA GPUs, AI, Omniverse, and HPC to digital-twin workflows.

Posted 8/10/2026full-timeRemote • California, Texas, Washington • 🇺🇸 United StatesSenior💰 $184,000 - $287,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in CAE/CFD/FEA simulation and GPU-accelerated computing, with a strong foundation in numerical methods and algorithm programming. Proficient in delivering technical training and collaborating with cross-functional teams to develop innovative engineering solutions.

Highest-signal resume keywords

CAE/CFD/FEA SimulationGPU-Accelerated ComputingAlgorithm Programming in Python and C/C++NVIDIA Omniverse and Digital Twin WorkflowsNumerical Methods and Physics Solvers

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Numerical SimulationSolver DevelopmentHPC-Based Engineering AnalysisPhysics-Informed Machine LearningSurrogate ModelingCUDA and CUDA-X Math LibrariesKubernetesDistributed TrainingData Pipeline DesignVersion Control with GitHub

Soft Skills

Solid Written and Oral CommunicationAdaptability in Fast-Paced Environments

Tools & Technologies

AnsysSiemens SimcenterAltairCOMSOLCadence Fidelity CFDOpenFOAMLS-DYNAAbaqusSlurmPCIe Accelerators

Industry Keywords

Mechanical EngineeringAerospace EngineeringCivil EngineeringChemical EngineeringComputational ScienceApplied MathematicsPhysics

Tech Stack

Tools & technologies

Distributed SystemsKubernetesPythonReact

About the role

Key responsibilities & impact

Support Business Development and Sales teams as part of a Solutions Architecture team, partnering with Industry Business leads, Account Managers, and Developer Relations managers across Industrial Engineering accounts.
Work directly with engineering-software developers and customer simulation teams in a customer-facing setting.
Help developers GPU-accelerate and scale CAE/CFD/FEA solvers and structural, thermal, and fluid-dynamics workloads on NVIDIA accelerated computing and HPC platforms.
Apply physics-informed ML and surrogate modeling, including NVIDIA PhysicsNeMo / Modulus, and NVIDIA Omniverse digital twins to compress design, simulation, and optimization cycles.
Analyze simulation and engineering application architectures and identify opportunities for acceleration.
Provide feedback and collaborate with engineering, product, and research teams.
Deliver trainings, hackathons, and technical demonstrations on NVIDIA solutions and platforms.

Requirements

What you’ll need

BS/MS/PhD in Mechanical, Aerospace, Civil, or Chemical Engineering, Computational Science, Applied Mathematics, Physics, or a related technical field (or equivalent experience)
8+ years working in CAE/CFD/FEA or computational engineering
Experience with numerical simulation, solver development, or HPC-based engineering analysis
Hands-on experience with commercial or open-source simulation tools such as Ansys, Siemens Simcenter, Altair, COMSOL, Cadence Fidelity CFD, OpenFOAM, LS-DYNA, or Abaqus
Strong grounding in numerical methods (FEM/FVM/spectral), linear algebra, and mathematics behind physics solvers
Algorithm programming experience using Python and C/C++
Familiarity with GPU-accelerating compute-intensive workloads
Familiarity with accelerated computing platforms, GPU-based distributed systems, and HPC clusters/schedulers such as Slurm
Familiarity with containers, numerical libraries, modular software design, version control, and GitHub
Experience designing, prototyping, and building complex customer solutions across data pipelines, solvers, compute, networking, and orchestration
Solid written and oral communication skills and familiarity with collaborative environments
Ability to learn, react, and adapt quickly in a fast-paced environment
Experience GPU-accelerating CFD/FEA solvers or developing physics-ML and surrogate models
Background with NVIDIA Omniverse, OpenUSD, and digital-twin workflows
Development experience with NVIDIA software libraries and GPUs, including CUDA and CUDA-X math libraries
Experience with Kubernetes, distributed training, and large-scale inference
Experience supporting or using PCIe accelerators such as GPUs, FPGAs, and DSPs from evaluation to production stages

Benefits

Comp & perks

Equity
Benefits
Equal opportunity employer
Inclusive work environment