Location: Quebec (Hybrid) - 3 days a week in office Engineering simulation is undergoing a structural shift. What used to be a slow, solver-driven process is becoming a fast, exploration-driven workflow, enabled by surrogate models, physics-informed AI, and GPU-accelerated methods.This changes not just how simulations run, but how engineers design—moving from evaluating a few options to exploring entire design spaces.We are looking for a hands-on Applied AI Engineer to experiment with these emerging technologies.

Your role is to try new tools, test them, benchmark them, integrate them into prototypes, determine what actually works in practice, and give an informed decision on its viability in Bentley’s products. The goal is to develop a clear understanding of where these approaches can realistically transform engineering workflows—and where they cannot.You will spend most of your time building and testing prototypes, not writing reports or maintaining production systems.Keep track of new technologies in the fieldEvaluate emerging simulation and AI tools through hands-on prototypingBuild and benchmark surrogate models against traditional FEM/CFD workflowsTest robustness, limitations, and failure modes of new approachesPrototype hybrid pipelines (e.G., surrogate + high-fidelity validation)Explore how these approaches can integrate into real engineering workflowsReport to stakeholders with clear recommendationsWhat We Are Looking For Strong programming ability (Python required)Experience with PyTorch, JAX, or similar ML frameworksSolid understanding of numerical simulation: Experience building experimental or research prototypesUnderstanding of design space exploration (DOE, parameter studies, combinatorial optimization)Exposure to physics-informed ML or scientific machine learningExperience working with GPU or high-performance computing environmentsExperience working with machine learning for physics simulationsHands-on experience developing Multiphysics simulation codeMindset Hands-on, experimental, and comfortable working with incomplete toolsCurious but skeptical—focuses on what works, not what is claimedNaturally tests limits and edge cases, not just happy pathsThinks in terms of workflows and iteration loops,



not isolated toolsAble to quickly move from idea → prototype → insightGood business acumen; You build prototypes that demonstrate current ways to accelerate design workflowsYou identify where AI-based approaches are genuinely useful—and where they failYou provide clear, evidence-based insights grounded in experiments, not assumptionsLieu : La simulation en ingénierie subit actuellement un changement structurel. Ce qui était auparavant un processus lent, axé sur les solveurs, devient un flux de travail rapide et axé sur l’exploration, rendu possible par les modèles de substitution (surrogate models), l’IA guidée par la physique (physics-informed AI) et les méthodes accélérées par processeur graphique (GPU).Cette évolution change non seulement la façon d'exécuter les simulations, mais aussi la manière dont les ingénieurs conçoivent les projets, passant de l’évaluation limitée de quelques options à l’exploration de l’ensemble des espaces de conception.L’objectif est de développer une compréhension claire de l’endroit où ces approches peuvent concrètement transformer les flux de travail en ingénierie — et de l’endroit où elles ne le peuvent pas.Assurer une veille technologique sur les nouveautés dans le domaine.Concevoir et comparer des modèles de substitution par rapport aux flux de travail traditionnels de FEM (méthode des éléments finis) et de CFD (dynamique des fluides numérique).Prototyper des pipelines hybrides (p.

Explorer comment intégrer ces approches dans de flux de travail d’ingénierie concrets.Fortes compétences en programmation (Python requis).Expérience avec PyTorch, JAX ou d’autres frameworks d’apprentissage automatique similaires.Solide compréhension de la simulation numérique : L’apprentissage profond, de la modélisation de substitution,



des modèles d’ordre réduit ou des flux de travail d’optimisation. Exposition à l’apprentissage automatique guidé par la physique (physics-informed ML) ou à l’apprentissage automatique scientifique (scientific ML).Expérience de travail avec des environnements GPU ou calcul haute performance (HPC).Expérience dans l’application de l’apprentissage automatique aux simulations physiques.Capacité à passer rapidement d’une idée → un prototype → des apprentissages concrets.Vous fournissez des perspectives claires et factuelles, basées sur des expériences concrètes et non sur des hypothèses.An attractive salary and benefits package.A commitment to inclusion, belonging and colleague wellbeing through global initiatives and resource groups.Be part of a company committed to making a real difference by advancing the world’s infrastructure for better quality of life, where your contributions help build a more sustainable, connected, and resilient world.

Bentley

Systems (Nasdaq: BSY) is the infrastructure engineering software company. We provide innovative software to advance the world’s infrastructure – sustaining both the global economy and environment. Our industry-leading software solutions are used by professionals, and organizations of every size, for the design, construction, and operations of roads and bridges, rail and transit, water and wastewater, public works and utilities, buildings and campuses, mining, and industrial facilities.

Our offerings, powered by the iTwin Platform for infrastructure digital twins, include MicroStation and Bentley Open applications for modeling and simulation, Seequent’s software for geoprofessionals, and Bentley Infrastructure Cloud encompassing ProjectWise for project delivery, SYNCHRO for construction management, and AssetWise for asset operations. Bentley is proud to be an equal opportunity employer and considers for employment all qualified applicants without regard to race, color, gender/gender identity, sexual orientation, disability, marital status, religion/belief, national origin, caste, age, or any other characteristic protected by local law or unrelated to job qualifications. #

📌 Applied Ai Engineer - Engineering Simulation & Design Exploration (Winnipeg)
🏢 Blyncsy
📍 Winnipeg