Senior Engineer - AI/ML

In this position...

Senior Engineer – AI-Accelerated CAE & Virtual Verification- A collaborative leader who can bridge the gap between traditional Analytical engineering and modern AI to create a more efficient, data drive- design loop. 

We are seeking a highly innovative and experienced Senior Engineer – AI-Accelerated CAE to lead the transformation of our virtual engineering and design processes. In this dual-focus role, you will leverage deep expertise in traditional physics-based simulation (FEA/CFD/Multi-body dynamics) to ensure product robustness and reduce physical testing. Simultaneously, you will pioneer the integration of state-of-the-art AI technologies—including Geometric Deep Learning, Physics-Informed Neural Networks (PINNs), and large-scale foundation models—to build surrogate models, accelerate the design loop, and drastically reduce computational wait times.
The ideal candidate is a proactive problem-solver who takes full ownership of initiatives, thrives in a collaborative team environment, and possesses the communication skills necessary to bridge the gap between traditional engineering and modern data science.

What you'll do...

AI-Driven Simulation & Acceleration:

• Geometric Deep Learning: Design and train advanced 3D machine learning models (e.g., Graph Neural Networks, 3D CNNs, Point Cloud processing) directly on CAD geometry and CAE mesh data to predict physical performance.
• Physics-AI Integration: Utilize advanced physics-informed machine learning frameworks to develop AI surrogate models that respect fundamental laws of physics while delivering near-real-time simulation results.
• Design Space Exploration: Integrate AI models into the generative design process, allowing design engineers to evaluate thousands of iterations instantly before committing to high-fidelity simulation.

Traditional CAE & High Performance Computing (HPC):

• High-Fidelity Simulation: Conduct complex, traditional CAE analyses (e.g., structural, thermal, crash, NVH, or aerodynamics) using industry-standard solvers (e.g., LS-DYNA, Abaqus, ANSYS, NASTRAN, OpenFOAM).
• HPC Utilization: Leverage and optimize workflows across High Performance Computing (HPC) clusters to efficiently scale both massive traditional simulations and the training of deep learning models.
• Virtual Verification: Drive the "Virtual-First" verification strategy by correlating CAE models with physical test data, improving simulation accuracy to confidently reduce physical prototyping time and costs.

Leadership, Ownership & Collaboration:

• Initiative Ownership: Take full end-to-end ownership of AI-CAE integration projects, from initial research and proof-of-concept to production deployment.
• Cross-Functional Teamwork: Act as a highly collaborative team player, seamlessly bridging the Mechanical/Aerospace Engineering, Data Science, and IT/HPC infrastructure teams.
• Communication: Translate and communicate highly complex AI and physics concepts clearly and effectively to both technical peers and non-technical leadership.
• al findings clearly through reports, presentations, and mentoring sessions.
• Proactive Ownership: A self-starter mentality with a history of identifying process bottlenecks and successfully driving innovative solutions.
• Team Player: Strong interpersonal skills with a commitment to fostering an inclusive, knowledge-sharing environment within a multidisciplinary team.

You'll have...

• Master’s degree or Ph.D. in Mechanical Engineering, Aerospace Engineering, Computer Science, Applied Mathematics, or a closely related field.
• 5+ years of industry experience in traditional CAE, computational mechanics, or CFD.

• 2+ years of hands-on experience applying Deep Learning / Machine Learning to engineering or physics problems.

   

Technical Skills (CAE & HPC):

• Expertise with pre/post-processing tools (e.g., ANSA, HyperMesh, Meta) and industry-standard solvers.
• Strong background in Classical CAE methods like FE, Implicit and explicit methods, CFD, CHT, and Multi Body Dynamics
• Proven track record of correlating simulation data with physical test results to validate engineering designs.