Computational Design Research Engineer
Foundation EGI · United States ·
- Work mode
- Remote
- Category
- Design
- Experience
- 5+ years
About Us:
On this Role:
This role is a mix of a few worlds coming together. You’ll be working with machine learning but not in a vacuum it’s applied to real engineering problems, working with 2D and 3D data from CAD, CAE, and CAM systems. A big part of the work is taking complex geometry, design workflows, and simulation data and figuring out how to turn that into something an AI system can actually understand and use.
We’re looking for someone who’s strong technically, especially in Python and ML, but also has the curiosity to dig into how things are designed and built in the real world. You might come from a research background or industry but either way you’re comfortable moving between theory and practical application. If you’ve spent time around mechanical systems or engineering design, that’s a big plus, because a lot of this role is about bridging that gap between advanced models and how engineers actually work day to day.
Responsibilities
- Design, develop, and maintain geometry processing and simulation algorithms for engineering applications.
- Build services for reading, processing, and writing 2D/3D engineering data.
- Develop rendering modules for generating 2D/3D visual assets.
- Curate and manage large-scale datasets for learning-based systems.
- Implement and optimize post-training workflows for machine learning models.
- Contribute to the development of domain-specific languages for engineering tasks.
What we are looking for
- 5+ years of academic or industry experience in one or more of the following areas: Geometric Processing, Simulation, Optimization, Machine Learning, or Domain-Specific Languages.
- Proficient in writing clean, modular, and maintainable Python code.
- Experience with dataset creation and data pipeline development.
- PhD or MS with a focus in Computational Design, Simulation, or AI.
- Experience developing CAD/CAM/CAE software tools.
- Experience developing or fine-tuning large language models (LLMs), including post-training methods such as quantization, pruning, distillation, or reinforcement learning.
- Experience designing or implementing DSLs or compilers.