Applied Ai Engineer - Engineering Simulation & Design Exploration

Location
Quebec (Hybrid) - 3 days a week in officeAbout the Role
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. This is an experimental role. You will spend most of your time building and testing prototypes, not writing reports or maintaining production systems.What You Will Do
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
Requirements:
Strong programming ability (Python required)Experience with PyTorch, JAX, or similar ML frameworksSolid understanding of numerical simulation: FEM, CFD, or multiphysics modelingFamiliarity with: Deep learning, surrogate modeling, reduced‑order models, or optimization workflowsGood communication skills and collaborative mindsetHighly Valued Experience
Building experimental or research prototypesUnderstanding of design space exploration (DOE, parameter studies, combinatorial optimization)Exposure to physics‑informed ML or scientific machine learningExperience 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;
able to understand whatbrings value to BentleyWhat Success Looks Like
You build prototypes that demonstrate new ways to accelerate design workflowsYou identify where AI‑based approaches are genuinely useful—and where they fail
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