Research Software Engineer- Simulation Algorithm/ Geometry Processing

Foundation Llm Technologies; United States; Teletrabalho; USD 140 000 - 200 000; Tempo integral

Resumo da oferta

Foundation Llm Technologies is building an applied AI co-pilot for design and manufacturing, bridging theory and real-world engineering challenges. You will work with 2D/3D CAD/CAE data, develop geometry processing and simulation algorithms, and contribute to data pipelines and ML workflows.

The role emphasizes strong Python and ML skills, with exposure to CAD/CAM/CAE systems and DSL/compiler efforts. Experience across academia or industry with a bridging mindset is valued.

Qualificações

• Experience with Code Development, Computational Design, or Geometry Processing.
• Strong Python and ML background; practical engineering knowledge preferred.
• Experience developing CAD/CAM/CAE software tools and LLMs is a plus.

Responsabilidades

• 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.

Conhecimentos

Python programming

Machine Learning

Geometric Processing

Simulation

Domain-Specific Languages

Data pipelines

Formação académica

PhD or MS in Computational Design/Simulation/AI

Ferramentas

CAD/CAM/CAE software tools

Descrição da oferta de emprego

REQUIRED: MUST have experience with Code Development + Computational Design and/or Geometry Processing. Applicants will not be considered without this experience.

About Us:

We are an MIT-born, venture-backed Silicon Valley startup building a real-life 'Jarvis'. An AI Copilot for design and manufacturing. Our goal is to utilize advanced AI, physics simulation, and computer graphics to reduce costs and improve engineering productivity across all steps of the design and manufacturing process.

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.

• 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.
• 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.