Research Scientist – Geometry, Machine Learning

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🔥 0 minutes ago

🍂 Massachusetts – Remote

🍂 Massachusetts – Remote

💵 $10k - $140k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧬 Research Scientist

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Foundation EGI

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2023

🤖 Artificial Intelligence

☁️ SaaS

🏢 Enterprise

Artificial Intelligence • SaaS • Enterprise

Foundation EGI is an AI-driven platform that automates the generation of engineering documentation from 3D CAD data. The company converts assemblies into 2D/3D parts catalogs, GD&T technical drawings, optimized assembly sequences, and multilingual operator/service manuals, aiming to reduce manual drafting and speed product development for manufacturing and engineering teams. Foundation EGI positions itself as an enterprise SaaS solution that brings machine learning and computational design into industrial workflows to improve accuracy, traceability, and time-to-production.

📋 Description

• 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.

🎯 Requirements

• 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. • BSc or MSc in Computer Science, Engineering, or a related field. • 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.